regressor

from sklearn.metrics import mean_squared_error, r2_score
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, HistGradientBoostingRegressor
from sklearn.naive_bayes import GaussianNB

!pip install catboost

from catboost import CatBoostRegressor

rf_regressor = RandomForestRegressor(n_estimators=1000, random_state=42)
rf_regressor.fit(x_train, y_train)
rf_pred = rf_regressor.predict(x_test)

# Evaluate the RandomForestRegressor
mse_rf = mean_squared_error(y_test, rf_pred)
r2_rf = r2_score(y_test, rf_pred)
print("Random Forest - Mean Squared Error: {}".format(mse_rf))
print("Random Forest - R-squared: {}".format(r2_rf))
print()

# AdaBoostRegressor
abc_regressor = AdaBoostRegressor()
abc_regressor.fit(x_train, y_train)
abc_pred = abc_regressor.predict(x_test)

# Evaluate the AdaBoostRegressor
mse_abc = mean_squared_error(y_test, abc_pred)
r2_abc = r2_score(y_test, abc_pred)
print("AdaBoost - Mean Squared Error: {}".format(mse_abc))
print("AdaBoost - R-squared: {}".format(r2_abc))
print()

# BaggingRegressor
bag_regressor = BaggingRegressor()
bag_regressor.fit(x_train, y_train)
bag_pred = bag_regressor.predict(x_test)

# Evaluate the BaggingRegressor
mse_bag = mean_squared_error(y_test, bag_pred)
r2_bag = r2_score(y_test, bag_pred)
print("Bagging - Mean Squared Error: {}".format(mse_bag))
print("Bagging - R-squared: {}".format(r2_bag))
print()

# HistGradientBoostingRegressor
hgbc_regressor = HistGradientBoostingRegressor()
hgbc_regressor.fit(x_train, y_train)
hgbc_pred = hgbc_regressor.predict(x_test)

# Evaluate the HistGradientBoostingRegressor
mse_hgbc = mean_squared_error(y_test, hgbc_pred)
r2_hgbc = r2_score(y_test, hgbc_pred)
print("Hist Gradient Boosting - Mean Squared Error: {}".format(mse_hgbc))
print("Hist Gradient Boosting - R-squared: {}".format(r2_hgbc))
print()

# GaussianNB (as a naive baseline for regression)
gnb_regressor = GaussianNB()
gnb_regressor.fit(x_train, y_train)
gnb_pred = gnb_regressor.predict(x_test)

# Evaluate the GaussianNB
mse_gnb = mean_squared_error(y_test, gnb_pred)
r2_gnb = r2_score(y_test, gnb_pred)
print("GaussianNB - Mean Squared Error: {}".format(mse_gnb))
print("GaussianNB - R-squared: {}".format(r2_gnb))
print()

# CatBoostRegressor
cbc_regressor = CatBoostRegressor(silent=True, depth=6, iterations=500)
cbc_regressor.fit(x_train, y_train)
cbc_pred = cbc_regressor.predict(x_test)

# Evaluate the CatBoostRegressor
mse_cbc = mean_squared_error(y_test, cbc_pred)
r2_cbc = r2_score(y_test, cbc_pred)
print("CatBoost - Mean Squared Error: {}".format(mse_cbc))
print("CatBoost - R-squared: {}".format(r2_cbc))