Add MLPClassifier to models
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@ -2,6 +2,7 @@ from numpy import mean
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from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score
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from sklearn.model_selection import cross_val_score
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neural_network import MLPClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.preprocessing import scale
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from sklearn.svm import LinearSVC
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@ -19,6 +20,8 @@ def choose_model(model):
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return KNeighborsClassifier(n_neighbors=10)
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elif model == "tree":
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return DecisionTreeClassifier(random_state=42)
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elif model == "neuralnet":
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return MLPClassifier(hidden_layer_sizes=10)
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def predict_data(data, target, model):
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@ -53,7 +56,7 @@ def evaluate_performance(confusion_matrix, accuracy, cv_score, auc):
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def main():
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data, target = parse_data(source="data/mamografia.csv", action="drop")
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predict_data(data=data, target=target, model="gnb")
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predict_data(data=data, target=target, model="neuralnet")
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if __name__ == "__main__":
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