In [1]:

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Importing packages for the DT.

In [3]:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Importing the breast cancer dataset.

In [4]:

dataset = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.3, random_state=42)

Instantiating and fitting a DT.

In [5]:

dt = DecisionTreeClassifier(criterion='entropy', max_depth=2, random_state=42)
dt.fit(X_train, y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=2, random_state=42)

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Plotting the tree structure.

In [6]:

plt.figure(figsize=(12, 8))
plot_tree(dt, feature_names=dataset.feature_names, class_names=dataset.target_names, filled=True, rounded=True)
plt.show()

Showing the importance of each feature.

In [7]:

plt.figure(figsize=(8, 5))
importances = dt.feature_importances_
plt.barh(dataset.feature_names, importances, color='skyblue')
plt.xlabel("Importance Score")
plt.show()

Printing the classifications reports of the model.

In [8]:

y_pred = dt.predict(X_test)

rep = classification_report(y_test, y_pred)
print(rep)

              precision    recall  f1-score   support

           0       0.84      0.97      0.90        63
           1       0.98      0.89      0.93       108

    accuracy                           0.92       171
   macro avg       0.91      0.93      0.91       171
weighted avg       0.93      0.92      0.92       171