From d25bc3107d22a2fc9a0d0aff8d29550e4e943345 Mon Sep 17 00:00:00 2001 From: abdoulayegk Date: Thu, 19 Nov 2020 07:49:40 +0530 Subject: [PATCH] adaboost classifier to predict brest cancer --- classification/adaboost_classifier.py | 39 +++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 classification/adaboost_classifier.py diff --git a/classification/adaboost_classifier.py b/classification/adaboost_classifier.py new file mode 100644 index 0000000..e0f23f0 --- /dev/null +++ b/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() \ No newline at end of file pFad - Phonifier reborn

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