def batch():
print("Tesing the accuracy of LinearRegression(batch)...")
# Train model
reg = LinearRegression()
reg.fit(X=X_train, y=y_train, lr=0.02, epochs=5000)
# Model accuracy
get_r2(reg, X_test, y_test)
def stochastic():
print("Tesing the accuracy of LinearRegression(stochastic)...")
# Train model
reg = LinearRegression()
reg.fit(X=X_train, y=y_train, lr=0.001, epochs=1000,
method="stochastic", sample_rate=0.5)
# Model accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy of KNN regressor...")
# Load data
X, y = load_boston_house_prices()
# 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
reg = KNeighborsRegressor()
reg.fit(X=X_train, y=y_train, k_neighbors=3)
# Model accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy of RegressionTree...")
# Load data
X, y = load_boston_house_prices()
# 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
reg = RegressionTree()
reg.fit(X=X_train, y=y_train, max_depth=5)
# Show rules
reg.rules
# Model accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy of GBDT regressor...")
# Load data
X, y = load_boston_house_prices()
# 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
reg = GradientBoostingRegressor()
reg.fit(X=X_train,
y=y_train,
n_estimators=4,
lr=0.5,
max_depth=2,
min_samples_split=2)
# Model accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy of Ridge Regressor(stochastic)...")
# Load data
X, y = load_boston_house_prices()
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=10)
# Train model
reg = Ridge()
reg.fit(X=X_train,
y=y_train,
lr=0.001,
epochs=1000,
method="stochastic",
sample_rate=0.5,
alpha=1e-7)
# Model accuracy
get_r2(reg, X_test, y_test)