regression metrics rmse

import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_log_error, mean_squared_log_error, mean_absolute_percentage_error


# Train a linear regression model
model = RandomForestRegressor()
model.fit(X_train_sscaler, y_train)

#Make predictions
y_pred = model.predict(X_test_sscaler)

#Calculate regression metrics

msle = mean_squared_log_error(y_test, y_pred)
rmsle = np.sqrt(msle)
mape = mean_absolute_percentage_error(y_test, y_pred)


print(f"MSLE: {mse:.2f}, nRMSLE: {rmsle:.2f}, nMAPE: {mape:.2f}")