def batch(): print("Tesing the performance of LogisticRegression(batch)...") # Train model clf = LogisticRegression() clf.fit(X=X_train, y=y_train, lr=0.05, epochs=200) # Model evaluation model_evaluation(clf, X_test, y_test)
def batch(): print("Tesing the performance of LogisticRegression(batch)...") # Train model clf = LogisticRegression() clf.fit(data=data_train, label=label_train, learning_rate=0.1, epochs=1000) # Model evaluation model_evaluation(clf, data_test, label_test) print(clf)
def stochastic(): print("Tesing the performance of LogisticRegression(stochastic)...") # Train model clf = LogisticRegression() clf.fit(data=data_train, label=label_train, learning_rate=0.01, epochs=100, method="stochastic", sample_rate=0.8) # Model evaluation model_evaluation(clf, data_test, label_test) print(clf)
def stochastic(): print("Tesing the performance of LogisticRegression(stochastic)...") # Train model clf = LogisticRegression() clf.fit(X=X_train, y=y_train, lr=0.01, epochs=100, method="stochastic", sample_rate=0.8) # Model evaluation model_evaluation(clf, X_test, y_test) print(clf)
def main(): print("Tesing the performance of RandomForest...") # Load data X, y = load_breast_cancer() # Split data randomly, train set rate 70% X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=40) # Train model rf = RandomForest() rf.fit(X_train, y_train, n_samples=300, max_depth=3, n_estimators=20) # Model evaluation model_evaluation(rf, X_test, y_test)
def main(): print("Tesing the performance of KNN classifier...") # Load data X, y = load_breast_cancer() X = min_max_scale(X) # Split data randomly, train set rate 70% X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=20) # Train model clf = KNeighborsClassifier() clf.fit(X_train, y_train, k_neighbors=21) # Model evaluation model_evaluation(clf, X_test, y_test)
def main(): print("Tesing the performance of DecisionTree...") # Load data X, y = load_breast_cancer() # Split data randomly, train set rate 70% X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10) # Train model clf = DecisionTree() clf.fit(X_train, y_train, max_depth=3) # Show rules clf.rules # Model evaluation model_evaluation(clf, X_test, y_test)
def main(): """Tesing the performance of DecisionTree """ print("Tesing the performance of DecisionTree...") # Load data data, label = load_breast_cancer() # Split data randomly, train set rate 70% data_train, data_test, label_train, label_test = train_test_split( data, label, random_state=10) # Train model clf = DecisionTree() clf.fit(data_train, label_train, max_depth=4) # Show rules print(clf) # Model evaluation model_evaluation(clf, data_test, label_test)
def main(): print("Tesing the performance of GBDT classifier...") # Load data X, y = load_breast_cancer() # Split data randomly, train set rate 70% X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=20) # Train model clf = GradientBoostingClassifier() clf.fit(X_train, y_train, n_estimators=2, lr=0.8, max_depth=3, min_samples_split=2) # Model evaluation model_evaluation(clf, X_test, y_test)
def main(): """Tesing the performance of RandomForest... """ print("Tesing the performance of RandomForest...") # Load data data, label = load_breast_cancer() # Split data randomly, train set rate 70% data_train, data_test, label_train, label_test = train_test_split( data, label, random_state=40) # Train model clf = RandomForest() clf.fit(data_train, label_train, n_estimators=50, max_depth=5, random_state=10) # Model evaluation model_evaluation(clf, data_test, label_test)
def main(): """Tesing the performance of GBDT classifier""" print("Tesing the performance of GBDT classifier...") # Load data data, label = load_breast_cancer() # Split data randomly, train set rate 70% data_train, data_test, label_train, label_test = train_test_split( data, label, random_state=20) # Train model clf = GradientBoostingClassifier() clf.fit(data_train, label_train, n_estimators=2, learning_rate=0.8, max_depth=3, min_samples_split=2) # Model evaluation model_evaluation(clf, data_test, label_test)