In [1]:

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execute:
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Importing packages for the RF.

In [2]:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Importing the breast cancer dataset.

In [3]:

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 RF.

In [4]:

rf = RandomForestClassifier(criterion='entropy', random_state=42)
rf.fit(X_train, y_train)

RandomForestClassifier(criterion='entropy', random_state=42)

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Printing the classifications reports of the model.

In [5]:

y_pred = rf.predict(X_test)

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

              precision    recall  f1-score   support

           0       0.98      0.94      0.96        63
           1       0.96      0.99      0.98       108

    accuracy                           0.97       171
   macro avg       0.97      0.96      0.97       171
weighted avg       0.97      0.97      0.97       171