def batch():
print("Tesing the accuracy of LogisticRegression(batch)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train, y=y_train, lr=0.05, epochs=200)
# Model accuracy
get_acc(clf, X_test, y_test)
def main():
print("Tesing the accuracy of KNN 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 = KNeighborsClassifier()
clf.fit(X_train, y_train, k_neighbors=21)
# Model accuracy
get_acc(clf, X_test, y_test)
def main():
print("Tesing the accuracy of Gaussian NaiveBayes...")
# 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 = GaussianNB()
clf.fit(X_train, y_train)
# Model accuracy
get_acc(clf, X_test, y_test)
def stochastic():
print("Tesing the accuracy of LogisticRegression(stochastic)...")
# Train model
clf = LogisticRegression()
clf.fit(X=X_train,
y=y_train,
lr=0.01,
epochs=200,
method="stochastic",
sample_rate=0.5)
# Model accuracy
get_acc(clf, X_test, y_test)
def main():
print("Tesing the accuracy 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 accuracy
get_acc(rf, X_test, y_test)
def main():
print("Tesing the accuracy 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 accuracy
get_acc(clf, X_test, y_test)
def main():
print("Tesing the accuracy 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 accuracy
get_acc(clf, X_test, y_test)
