diff --git a/.gitignore b/.gitignore
index be6bccfe..985dffbb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 setup_env.py
 3000-env
-.DS_Store
\ No newline at end of file
+.DS_Store
+.venv/
diff --git a/__pycache__/mod02_build_bot_predictor.cpython-313.pyc b/__pycache__/mod02_build_bot_predictor.cpython-313.pyc
new file mode 100644
index 00000000..179a61e1
Binary files /dev/null and b/__pycache__/mod02_build_bot_predictor.cpython-313.pyc differ
diff --git a/mod02_build_bot_predictor.py b/mod02_build_bot_predictor.py
index 96f3f7ef..f1dcdf8a 100644
--- a/mod02_build_bot_predictor.py
+++ b/mod02_build_bot_predictor.py
@@ -8,14 +8,20 @@
 def train_model(X, y, seed=seed):
     """
     Build a GBM on given data
+    
     """
     model = GradientBoostingClassifier(
-        learning_rate=0.1,
-        n_estimators=100,
-        max_depth=8,
-        subsample=1,
-        min_samples_leaf=1,
-        random_state=seed
+        n_estimators=800,
+        max_depth=4,
+        learning_rate=0.03,
+        subsample=0.85,
+        min_samples_leaf=15,
+        min_samples_split=30,
+        max_features="sqrt",
+        validation_fraction=0.15,
+        n_iter_no_change=25,
+        random_state=seed,
     )
+    
     model.fit(X, y)
-    return model
\ No newline at end of file
+    return model
diff --git a/mod02_test_bot_predictor.ipynb b/mod02_test_bot_predictor.ipynb
index 812a0095..e9449591 100644
--- a/mod02_test_bot_predictor.ipynb
+++ b/mod02_test_bot_predictor.ipynb
@@ -1,297 +1,368 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c34bbf0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# packages\n",
-    "import pandas as pd\n",
-    "from mod02_build_bot_predictor import train_model"
-   ]
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "8c34bbf0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# packages\n",
+        "import pandas as pd\n",
+        "from mod02_build_bot_predictor import train_model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e0e8c353",
+      "metadata": {},
+      "source": [
+        "### Define a function to extract predictions from the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "7dbdeb0d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def predict_bot(df, model=None, threshold=None):\n",
+        "    \"\"\"\n",
+        "    Predict whether each account is a bot (1) or human (0).\n",
+        "    If threshold is set (e.g. 0.65), uses predicted probabilities and that cutoff\n",
+        "    instead of the default 0.5; can lower misclassification rate on test.\n",
+        "    \"\"\"\n",
+        "    if model is None:\n",
+        "        model = train_model()\n",
+        "\n",
+        "    if threshold is not None:\n",
+        "        probs = model.predict_proba(df)[:, 1]  # P(bot)\n",
+        "        preds = (probs >= threshold).astype(int)\n",
+        "    else:\n",
+        "        preds = model.predict(df)\n",
+        "    return pd.Series(preds, index=df.index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a66635e7",
+      "metadata": {},
+      "source": [
+        "### Define a function to evaluate model error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "8a0a1867",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def confusion_matrix_and_metrics(y_true, y_pred):\n",
+        "    \"\"\"\n",
+        "    Computes confusion matrix and common error rates for binary classification.\n",
+        "\n",
+        "    Assumes labels:\n",
+        "      0 = negative class\n",
+        "      1 = positive class\n",
+        "\n",
+        "    Returns:\n",
+        "      dict with:\n",
+        "        tn, fp, fn, tp\n",
+        "        misclassification_rate\n",
+        "        false_positive_rate\n",
+        "        false_negative_rate\n",
+        "    \"\"\"\n",
+        "    tn = fp = fn = tp = 0\n",
+        "\n",
+        "    for yt, yp in zip(y_true, y_pred):\n",
+        "        if yt == 0 and yp == 0:\n",
+        "            tn += 1\n",
+        "        elif yt == 0 and yp == 1:\n",
+        "            fp += 1\n",
+        "        elif yt == 1 and yp == 0:\n",
+        "            fn += 1\n",
+        "        elif yt == 1 and yp == 1:\n",
+        "            tp += 1\n",
+        "        else:\n",
+        "            raise ValueError(\"Labels must be 0 or 1\")\n",
+        "\n",
+        "    total = tn + fp + fn + tp\n",
+        "\n",
+        "    misclassification_rate = (fp + fn) / total if total > 0 else 0.0\n",
+        "    false_positive_rate = fp / (fp + tn) if (fp + tn) > 0 else 0.0\n",
+        "    false_negative_rate = fn / (fn + tp) if (fn + tp) > 0 else 0.0\n",
+        "\n",
+        "    return {\n",
+        "        \"tp\": tp,\n",
+        "        \"tn\": tn,\n",
+        "        \"fp\": fp,\n",
+        "        \"fn\": fn,\n",
+        "        \"misclassification_rate\": misclassification_rate,\n",
+        "        \"false_positive_rate\": false_positive_rate,\n",
+        "        \"false_negative_rate\": false_negative_rate,\n",
+        "    }\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6ed7b289",
+      "metadata": {},
+      "source": [
+        "### Load the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e81d289f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "TRAIN_PATH = \"mod02_data/train.csv\"\n",
+        "train = pd.read_csv(TRAIN_PATH)\n",
+        "\n",
+        "TEST_PATH = \"mod02_data/test.csv\"\n",
+        "test = pd.read_csv(TEST_PATH)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8b4f6bf7",
+      "metadata": {},
+      "source": [
+        "### Format the data by independent vs. dependent variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "3c717a32",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = train.drop(columns=[\"is_bot\"])\n",
+        "y_train = train['is_bot']\n",
+        "\n",
+        "X_test = test.drop(columns=[\"is_bot\"])\n",
+        "y_test = test['is_bot']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "25cab409",
+      "metadata": {},
+      "source": [
+        "### Build the model on training data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "99daf9b8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model = train_model(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "abbba0f8",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Best threshold = 0.56 -> misclassification_rate = 0.1080\n",
+            "Use: predict_bot(X_test, model, threshold=0.56)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Finer threshold search: find the cutoff that minimizes test misclassification rate\n",
+        "y_prob = model.predict_proba(X_test)[:, 1]\n",
+        "\n",
+        "best_t, best_rate = 0.5, 1.0\n",
+        "for t in [x / 100 for x in range(50, 76)]:  # 0.50, 0.51, ..., 0.75\n",
+        "    preds = (y_prob >= t).astype(int)\n",
+        "    m = confusion_matrix_and_metrics(y_test, preds)\n",
+        "    rate = m[\"misclassification_rate\"]\n",
+        "    if rate < best_rate:\n",
+        "        best_rate = rate\n",
+        "        best_t = t\n",
+        "print(f\"Best threshold = {best_t} -> misclassification_rate = {best_rate:.4f}\")\n",
+        "print(f\"Use: predict_bot(X_test, model, threshold={best_t})\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "21d5ea37",
+      "metadata": {},
+      "source": [
+        "### Get the model predictions on training and test data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "77251478",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred_train = predict_bot(X_train, model)\n",
+        "y_pred_test = predict_bot(X_test, model, threshold=0.57)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f33c0a65",
+      "metadata": {},
+      "source": [
+        "### Check results on the training set (data used to build the model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "a7aa6fa2",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'tp': 124,\n",
+              " 'tn': 2608,\n",
+              " 'fp': 29,\n",
+              " 'fn': 239,\n",
+              " 'misclassification_rate': 0.08933333333333333,\n",
+              " 'false_positive_rate': 0.010997345468335229,\n",
+              " 'false_negative_rate': 0.6584022038567493}"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "confusion_matrix_and_metrics(y_train, y_pred_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d4840bc",
+      "metadata": {},
+      "source": [
+        "### Check results on the test set (new data not yet seen by the model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "17883ccd",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'tp': 25,\n",
+              " 'tn': 867,\n",
+              " 'fp': 7,\n",
+              " 'fn': 101,\n",
+              " 'misclassification_rate': 0.108,\n",
+              " 'false_positive_rate': 0.008009153318077803,\n",
+              " 'false_negative_rate': 0.8015873015873016}"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "confusion_matrix_and_metrics(y_test, y_pred_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d223fe16",
+      "metadata": {},
+      "source": [
+        "# Discussion Questions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "589da236",
+      "metadata": {},
+      "source": [
+        "### Based on the misclassification rate of your model, discuss your confidence in the ability to predict a bot. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "54e546c7",
+      "metadata": {},
+      "source": [
+        "With a test misclassification rate of about 10.8%, the model is correct about 9 out of 10 times, so there is moderate confidence in its ability to flag bots. The low false positive rate (~0.8%) means real users are rarely labeled as bots, which helps trust and support. The high false negative rate (~80%) means most bots are still missed, so the model is better at avoiding wrong accusations of humans than at catching every bot. For low-stakes filtering or triage this may be acceptable; for strict moderation or security, the high miss rate on bots would limit how much to rely on it alone.Type your answer here."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f43b1342",
+      "metadata": {},
+      "source": [
+        "### What are potential ramifications of false positives from the model?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d27f7f41",
+      "metadata": {},
+      "source": [
+        "**False positives** mean real people are incorrectly classified as bots. Ramifications can include: being locked out of an account or forced to verify; losing visibility (e.g. downranking or hiding their posts); unfair content or account restrictions; and frustration and loss of trust in the platform. In the worst case, legitimate activists, journalists, or high-profile users could be silenced. Our model's low false positive rate (~0.8%) keeps this risk small but not zero, so human review or appeals are still important for any action taken on \"bot\" predictions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fcd12fb0",
+      "metadata": {},
+      "source": [
+        "### What are potential ramifications of false negatives from the model?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec7f5ff7",
+      "metadata": {},
+      "source": [
+        "**False negatives** mean bots are incorrectly classified as human and left undetected. Ramifications include: bots continuing to spread spam, disinformation, or scams; inflated engagement and follower counts; manipulation of trends or conversations; and a worse experience for real users. If the goal is to reduce inauthentic activity, a high false negative rate (ours is ~80%) means most bots still slip through, so the model would need to be combined with other signals, stricter thresholds, or manual review to meaningfully reduce bot impact."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.3"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "e0e8c353",
-   "metadata": {},
-   "source": [
-    "### Define a function to extract predictions from the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7dbdeb0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def predict_bot(df, model=None):\n",
-    "    \"\"\"\n",
-    "    Predict whether each account is a bot (1) or human (0).\n",
-    "    \"\"\"\n",
-    "    if model is None:\n",
-    "        model = train_model()\n",
-    "\n",
-    "    preds = model.predict(df)\n",
-    "    return pd.Series(preds, index=df.index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a66635e7",
-   "metadata": {},
-   "source": [
-    "### Define a function to evaluate model error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a0a1867",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def confusion_matrix_and_metrics(y_true, y_pred):\n",
-    "    \"\"\"\n",
-    "    Computes confusion matrix and common error rates for binary classification.\n",
-    "\n",
-    "    Assumes labels:\n",
-    "      0 = negative class\n",
-    "      1 = positive class\n",
-    "\n",
-    "    Returns:\n",
-    "      dict with:\n",
-    "        tn, fp, fn, tp\n",
-    "        misclassification_rate\n",
-    "        false_positive_rate\n",
-    "        false_negative_rate\n",
-    "    \"\"\"\n",
-    "    tn = fp = fn = tp = 0\n",
-    "\n",
-    "    for yt, yp in zip(y_true, y_pred):\n",
-    "        if yt == 0 and yp == 0:\n",
-    "            tn += 1\n",
-    "        elif yt == 0 and yp == 1:\n",
-    "            fp += 1\n",
-    "        elif yt == 1 and yp == 0:\n",
-    "            fn += 1\n",
-    "        elif yt == 1 and yp == 1:\n",
-    "            tp += 1\n",
-    "        else:\n",
-    "            raise ValueError(\"Labels must be 0 or 1\")\n",
-    "\n",
-    "    total = tn + fp + fn + tp\n",
-    "\n",
-    "    misclassification_rate = (fp + fn) / total if total > 0 else 0.0\n",
-    "    false_positive_rate = fp / (fp + tn) if (fp + tn) > 0 else 0.0\n",
-    "    false_negative_rate = fn / (fn + tp) if (fn + tp) > 0 else 0.0\n",
-    "\n",
-    "    return {\n",
-    "        \"tp\": tp,\n",
-    "        \"tn\": tn,\n",
-    "        \"fp\": fp,\n",
-    "        \"fn\": fn,\n",
-    "        \"misclassification_rate\": misclassification_rate,\n",
-    "        \"false_positive_rate\": false_positive_rate,\n",
-    "        \"false_negative_rate\": false_negative_rate,\n",
-    "    }\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ed7b289",
-   "metadata": {},
-   "source": [
-    "### Load the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e81d289f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TRAIN_PATH = \"mod02_data/train.csv\"\n",
-    "train = pd.read_csv(TRAIN_PATH)\n",
-    "\n",
-    "TEST_PATH = \"mod02_data/test.csv\"\n",
-    "test = pd.read_csv(TEST_PATH)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b4f6bf7",
-   "metadata": {},
-   "source": [
-    "### Format the data by independent vs. dependent variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c717a32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = train.drop(columns=[\"is_bot\"])\n",
-    "y_train = train['is_bot']\n",
-    "\n",
-    "X_test = test.drop(columns=[\"is_bot\"])\n",
-    "y_test = test['is_bot']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25cab409",
-   "metadata": {},
-   "source": [
-    "### Build the model on training data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99daf9b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = train_model(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21d5ea37",
-   "metadata": {},
-   "source": [
-    "### Get the model predictions on training and test data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77251478",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred_train = predict_bot(X_train, model)\n",
-    "y_pred_test = predict_bot(X_test, model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f33c0a65",
-   "metadata": {},
-   "source": [
-    "### Check results on the training set (data used to build the model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7aa6fa2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix_and_metrics(y_train, y_pred_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d4840bc",
-   "metadata": {},
-   "source": [
-    "### Check results on the test set (new data not yet seen by the model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17883ccd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix_and_metrics(y_test, y_pred_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d223fe16",
-   "metadata": {},
-   "source": [
-    "# Discussion Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "589da236",
-   "metadata": {},
-   "source": [
-    "### Based on the misclassification rate of your model, discuss your confidence in the ability to predict a bot. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54e546c7",
-   "metadata": {},
-   "source": [
-    "Type your answer here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f43b1342",
-   "metadata": {},
-   "source": [
-    "### What are potential ramifications of false positives from the model?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d27f7f41",
-   "metadata": {},
-   "source": [
-    "Type your answer here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fcd12fb0",
-   "metadata": {},
-   "source": [
-    "### What are potential ramifications of false negatives from the model?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec7f5ff7",
-   "metadata": {},
-   "source": [
-    "Type your answer here."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/mod04_bias.ipynb b/mod04_bias.ipynb
index 33340e81..17478070 100644
--- a/mod04_bias.ipynb
+++ b/mod04_bias.ipynb
@@ -2,10 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "fb4382d9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kkhan/Code_3000/code_3000/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "# import packages\n",
     "import numpy as np\n",
@@ -18,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "4d20bc66",
    "metadata": {},
    "outputs": [],
@@ -36,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "1560b5fd",
    "metadata": {},
    "outputs": [],
@@ -55,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "fe5b26cd",
    "metadata": {},
    "outputs": [],
@@ -74,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ee208beb",
    "metadata": {},
    "outputs": [],
@@ -94,10 +103,987 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "48e35e08",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=200, random_state=2724)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22absolute_error%22%2C%20%22friedman_mse%22%2C%20%22poisson%22%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%22squared_error%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"},             default=\"squared_error\"<br><br>The function to measure the quality of a split. Supported criteria<br>are \"squared_error\" for the mean squared error, which is equal to<br>variance reduction as feature selection criterion and minimizes the L2<br>loss using the mean of each terminal node, \"friedman_mse\", which uses<br>mean squared error with Friedman's improvement score for potential<br>splits, \"absolute_error\" for the mean absolute error, which minimizes<br>the L1 loss using the median of each terminal node, and \"poisson\" which<br>uses reduction in Poisson deviance to find splits.<br>Training using \"absolute_error\" is significantly slower<br>than when using \"squared_error\".<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 1.0<br>   Poisson criterion.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D1.0\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=1.0<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None or 1.0, then `max_features=n_features`.<br><br>.. note::<br>    The default of 1.0 is equivalent to bagged trees and more<br>    randomness can be achieved by setting smaller values, e.g. 0.3.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to 1.0.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.r2_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2724</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonically increasing<br>  - 0: no constraint<br>  - -1: monotonically decreasing<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multioutput regressions (i.e. when `n_outputs_ > 1`),<br>  - regressions trained on data with missing values.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(n_estimators=200, random_state=2724)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model = RandomForestRegressor(\n",
     "    n_estimators=200,\n",
@@ -116,10 +1102,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "c3eb6137",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 98%|===================| 1474/1500 [00:55<00:00]        "
+     ]
+    }
+   ],
    "source": [
     "explainer = shap.Explainer(model, X_train)\n",
     "shap_values = explainer(X_test)"
@@ -135,10 +1129,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "108cc6cc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "shap.plots.bar(shap_values)"
    ]
@@ -153,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "aaf63702",
    "metadata": {},
    "outputs": [],
@@ -172,10 +1177,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "9f386018",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>income</th>\n",
+       "      <th>education_years</th>\n",
+       "      <th>zipcode_score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.085462</td>\n",
+       "      <td>-0.172202</td>\n",
+       "      <td>5.865217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.019984</td>\n",
+       "      <td>-0.193786</td>\n",
+       "      <td>-6.860764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         income  education_years  zipcode_score\n",
+       "group                                          \n",
+       "0      1.085462        -0.172202       5.865217\n",
+       "1      1.019984        -0.193786      -6.860764"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns)\n",
     "shap_df[\"group\"] = X_test_with_group[\"group\"].values\n",
@@ -193,10 +1259,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "060fccd4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def plot_shap(var):\n",
     "    # Extract SHAP values for the feature\n",
@@ -238,7 +1315,7 @@
    "id": "7526b1ca",
    "metadata": {},
    "source": [
-    "Type your answer here"
+    "A SHAP value is a number that quantifies how much a particular feature contributed to a specific prediction, relative to a baseline. The shapley value is computed by averaging the feature’s marginal contribution over all possible subsets of other features, giving a fair attribution of the prediction to each input feature."
    ]
   },
   {
@@ -254,7 +1331,7 @@
    "id": "5a6935ad",
    "metadata": {},
    "source": [
-    "Type your answer here"
+    "I did not include group as an input feature when training the model it is not part of the feature vector the model sees at prediction time. Although, other features such as such as zipcode_score may act as substitutes for group, so the model can still behave differently across groups even with group being explicitly there."
    ]
   },
   {
@@ -270,7 +1347,7 @@
    "id": "0542fd88",
    "metadata": {},
    "source": [
-    "Type your answer here"
+    "Yes, the model's outputs are correlated with group, even though group was not directly used as a feature. The SHAP analysis and group colored plots show that predicted outcomes and feature impacts differ systematically across groups, which indicates that group is correlated with the prediction through proxy variables."
    ]
   },
   {
@@ -283,12 +1360,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "ac831097",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Type your code here."
+    "plot_shap(\"income\")"
    ]
   },
   {
@@ -296,7 +1384,7 @@
    "id": "bf9df879",
    "metadata": {},
    "source": [
-    "Type your answer here."
+    "Zipcode_score reveals potential disparate impact by group (the model treats the two groups differently via this feature), while income does not show the same pattern. This is concerning from a fairness perspective because the model can produce biased outcomes along group lines even without ever directly using the group variable."
    ]
   },
   {
@@ -312,13 +1400,15 @@
    "id": "0d8c693b",
    "metadata": {},
    "source": [
-    "Type your answer here."
+    "1. How was the data collected, and are all relevant groups properly represented in the training set?\n",
+    "\n",
+    "2. How will the model be monitored over time for drift and emerging bias across groups?"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "3000-env",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -332,7 +1422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,