variance in machine learning

from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor

# Train a Random Forest model
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Evaluate the model using cross-validation
cv_scores_rf = cross_val_score(rf, X_train, y_train, cv=5, scoring='neg_mean_squared_error')
cv_scores_rf = -cv_scores_rf

# Calculate the average MSE and its standard deviation
avg_mse_rf = cv_scores_rf.mean()
std_mse_rf = cv_scores_rf.std()

print(f"Random Forest Cross-Validation MSE: {avg_mse_rf:.2f} +/- {std_mse_rf:.2f}")

# Train a Gradient Boosting model
gb = GradientBoostingRegressor(n_estimators=100, random_state=42)
gb.fit(X_train, y_train)

# Evaluate the model using cross-validation
cv_scores_gb = cross_val_score(gb, X_train, y_train, cv=5, scoring='neg_mean_squared_error')
cv_scores_gb = -cv_scores_gb

# Calculate the average MSE and its standard deviation
avg_mse_gb = cv_scores_gb.mean()
std_mse_gb = cv_scores_gb.std()

print(f"Gradient Boosting Cross-Validation MSE: {avg_mse_gb:.2f} +/- {std_mse_gb:.2f}")