adaboost classifier to predict Brest cancer

classification/adaboost_classifier.py

@@ -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()