I really need assistance,I have used catboost and this my last code from catboost import CatBoostRegressor

from sklearn.model_selection import RandomizedSearchCV

import numpy as np

# Define the parameter grid to search

params = {

'iterations': np.arange(500, 2000, 100),

'learning_rate': np.arange(0.01, 0.3, 0.01),

'depth': np.arange(3, 16),

'l2_leaf_reg': np.arange(1, 10),

'random_strength': np.arange(0, 5),

'border_count': np.arange(5, 100, 5),

}

# Define the model to use

model = CatBoostRegressor()

# Define the random search object

rs = RandomizedSearchCV(model, param_distributions=params, cv=5, n_iter=50, n_jobs=-1, random_state=42, verbose=3)

# Fit the random search object to the data

rs.fit(X_train, y_train) however i have calculated mse and rmse but when it comes to predicting weighted_cpi iam getting errors...Can i share my code to someone?he/she can help me figure ou where iam going wrong..hit my inbox kindly!

# Print the best hyperparameters

print(rs.best_params_)