Nov 19, 2020
diff --git a/classification/adaboost_classifier.py b/classification/adaboost_classifier.py
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.datasets import load_breast_cancer
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import plot_confusion_matrix
 from matplotlib import pyplot as plt


 """Adaboost classifier example"""


 def adaboost():
    cancer_df = load_breast_cancer()
    print(cancer_df.keys())
    X, y = cancer_df.data, cancer_df.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

    abc = AdaBoostClassifier(base_estimator=None,
                             n_estimators=300, learning_rate=1, random_state=0)
    abc.fit(X_train, y_train)
    y_pred = abc.predict(X_test)
    print(y_pred[:20])
    # Display Confusion Matrix of Classifier
    plot_confusion_matrix(
        abc,
        X_test,
        y_test,
        display_labels=cancer_df["target_names"],
        cmap="Blues",
        normalize="true",
    )
    plt.title("Normalized Confusion Matrix - Cancer Dataset")
    plt.show()

    # to see the accuracy of the model
    print("Accuracy of adaboost is:", abc.score(X_test, y_test))


 if __name__ == "__main__":
    adaboost()
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,39 @@
		from sklearn.ensemble import AdaBoostClassifier
		from sklearn.datasets import load_breast_cancer
		from sklearn.model_selection import train_test_split
		from sklearn.metrics import plot_confusion_matrix
		from matplotlib import pyplot as plt


		"""Adaboost classifier example"""


		def adaboost():
		cancer_df = load_breast_cancer()
		print(cancer_df.keys())
		X, y = cancer_df.data, cancer_df.target
		X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

		abc = AdaBoostClassifier(base_estimator=None,
		n_estimators=300, learning_rate=1, random_state=0)
		abc.fit(X_train, y_train)
		y_pred = abc.predict(X_test)
		print(y_pred[:20])
		# Display Confusion Matrix of Classifier
		plot_confusion_matrix(
		abc,
		X_test,
		y_test,
		display_labels=cancer_df["target_names"],
		cmap="Blues",
		normalize="true",
		)
		plt.title("Normalized Confusion Matrix - Cancer Dataset")
		plt.show()

		# to see the accuracy of the model
		print("Accuracy of adaboost is:", abc.score(X_test, y_test))


		if __name__ == "__main__":
		adaboost()