diff --git a/pygam/core.py b/pygam/core.py
index ce27efbc..2e972e7e 100644
--- a/pygam/core.py
+++ b/pygam/core.py
@@ -1,6 +1,7 @@
 """Core Classes"""
 
 import numpy as np
+from copy import deepcopy
 
 from pygam.utils import flatten, round_to_n_decimal_places
 
@@ -150,15 +151,15 @@ def get_params(self, deep=False):
         -------
         dict
         """
-        attrs = self.__dict__
+        attrs =deepcopy(self.__dict__)
         for attr in self._include:
-            attrs[attr] = getattr(self, attr)
+            attrs[attr] = deepcopy(getattr(self, attr))
 
         if deep is True:
             return attrs
         return dict(
             [
-                (k, v)
+                (k, deepcopy(v))
                 for k, v in list(attrs.items())
                 if (k[0] != "_") and (k[-1] != "_") and (k not in self._exclude)
             ]
diff --git a/pygam/links.py b/pygam/links.py
index cc976eec..c14d27a1 100644
--- a/pygam/links.py
+++ b/pygam/links.py
@@ -118,8 +118,19 @@ def mu(self, lp, dist):
         -------
         mu : np.array of length n
         """
-        elp = np.exp(lp)
-        return dist.levels * elp / (elp + 1)
+        lp = np.asarray(lp)
+        mu = np.empty_like(lp, dtype=float)
+
+# positive branch
+        pos = lp >= 0
+        mu[pos] = dist.levels / (1 + np.exp(-lp[pos]))
+
+# negative branch
+        neg = ~pos
+        exp_lp = np.exp(lp[neg])
+        mu[neg] = dist.levels * exp_lp / (1 + exp_lp)
+
+        return mu
 
     def gradient(self, mu, dist):
         """
diff --git a/pygam/pygam.py b/pygam/pygam.py
index 6f86ae43..f6e0bf19 100644
--- a/pygam/pygam.py
+++ b/pygam/pygam.py
@@ -8,6 +8,7 @@
 import scipy as sp
 from progressbar import ProgressBar
 from scipy import stats  # noqa: F401
+from sklearn.base import BaseEstimator
 
 from pygam.callbacks import (
     CALLBACKS,  # noqa: F401
@@ -175,7 +176,9 @@ def __init__(
         self.link = link
         self.callbacks = callbacks
         self.verbose = verbose
-        self.terms = TermList(terms) if isinstance(terms, Term) else terms
+        self.terms = terms  # store original input for get_params()
+        self._terms = TermList(terms) if isinstance(terms, Term) else terms  # internal processed version
+
         self.fit_intercept = fit_intercept
 
         for k, v in kwargs.items():
@@ -207,6 +210,14 @@ def __init__(
     #         self.terms.lam = value
     #     else:
     #         self._lam = value
+    def _get_tags(self):
+        """Required for sklearn compatibility"""
+        return {
+          "requires_y": True,
+          "non_deterministic": False,
+          "requires_positive_y": False,
+    }
+
 
     @property
     def _is_fitted(self):
@@ -856,6 +867,7 @@ def _on_loop_end(self, variables):
         for callback in self.callbacks:
             if hasattr(callback, "on_loop_end"):
                 self.logs_[str(callback)].append(callback.on_loop_end(**variables))
+                
 
     def fit(self, X, y, weights=None):
         """Fit the generalized additive model.
diff --git a/test.py b/test.py
new file mode 100644
index 00000000..f1da53a0
--- /dev/null
+++ b/test.py
@@ -0,0 +1,3 @@
+from pygam import LinearGAM
+gam = LinearGAM()
+print(gam._get_tags())
\ No newline at end of file
diff --git a/test1.py b/test1.py
new file mode 100644
index 00000000..6edce486
--- /dev/null
+++ b/test1.py
@@ -0,0 +1,14 @@
+from pygam import LinearGAM
+
+gam = LinearGAM()
+
+params = gam.get_params()
+
+print("Before change:", params["callbacks"])
+
+# Try modifying returned params
+params["callbacks"].append("hack")
+
+# Check again
+params2 = gam.get_params()
+print("After change:", params2["callbacks"])
\ No newline at end of file
diff --git a/test5.py b/test5.py
new file mode 100644
index 00000000..c37173e9
--- /dev/null
+++ b/test5.py
@@ -0,0 +1,33 @@
+import numpy as np
+from pygam import LinearGAM
+
+X = np.random.rand(100, 1)
+y = np.sin(X[:, 0])
+
+gam = LinearGAM().fit(X, y)
+
+print("Model trained successfully\n")
+
+try:
+    result = gam.predict([[0.5], [0.2]])
+    print("Test 1 Passed: Valid input works")
+except Exception as e:
+    print("Test 1 Failed:", e)
+
+try:
+    result = gam.predict([0.5, 0.2])
+    print("Test 2 Passed: 1D input handled")
+except Exception as e:
+    print("Test 2 Failed:", e)
+
+try:
+    gam.predict([[1, 2], [3, 4]])
+    print("Test 3 Failed: Should have raised error")
+except ValueError as e:
+    print("Test 3 Passed:", e)
+
+try:
+    gam.predict([[0.5], [np.nan]])
+    print("Test 4 Failed: Should have raised error")
+except ValueError as e:
+    print("Test 4 Passed:", e)
\ No newline at end of file