python-ml/decisionTree1.py at main · haticeeakgull/python-ml · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.inspection import DecisionBoundaryDisplay
import numpy as np
import matplotlib.pyplot as plt
iris = load_iris()

n_classes=len(iris.target_names)
plot_colors="ryb"

for pairidx , pair in enumerate([[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]]):
    X = iris.data[:, pair]
    y= iris.target

    clf = DecisionTreeClassifier().fit(X,y)
    ax= plt.subplot(2,3,pairidx+1)
    plt.tight_layout(h_pad=0.5, w_pad=0.5,pad=2.5)
    DecisionBoundaryDisplay.from_estimator(clf,X,cmap=plt.cm.RdYlBu,response_method="predict",ax=ax,xlabel=iris.feature_names[pair[0]],ylabel=iris.feature_names[pair[1]])

    for i ,color in zip(range(n_classes),plot_colors):
        idx=np.where(y==i)
        plt.scatter(X[idx,0], X[idx,1],c=color,label=iris.target_names[i],cmap=plt.cm.RdYlBu,edgecolors="black")
plt.show()