CODEWITHSUNDEEP
pythonrandom-forestgrid-search
raffa_sa
raffa_sa

Reputation: 425

Random Forest tuning with RandomizedSearchCV

I have a few questions concerning Randomized grid search in a Random Forest Regression Model. My parameter grid looks like this:

random_grid = {'bootstrap': [True, False],
               'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],
               'max_features': ['auto', 'sqrt'],
               'min_samples_leaf': [1, 2, 4],
               'min_samples_split': [2, 5, 10],
               'n_estimators': [130, 180, 230]}

and my code for the RandomizedSearchCV like this:

# Use the random grid to search for best hyperparameters
# First create the base model to tune
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()
# Random search of parameters, using 3 fold cross validation, 
# search across 100 different combinations, and use all available cores
rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)
# Fit the random search model
rf_random.fit(X_1, Y)

is there any way to calculate the Root mean square at each parameter set? This would be more interesting to me as the R^2 score? If I now want to get the best parameter set, as printed underneath i would also use the lowest RMSE score. Is there any way to do that?

rf_random.best_params_
rf_random.best_score_
rf_random.best_estimator_

thank you, R

Upvotes: 7

Views: 23577

Answers (3)

grey
grey

Reputation: 357

Maybe this will help you.

`
rf_grid = {"n_estimators": np.arange(10, 100, 10),<br>
           "max_depth": [None, 3, 5, 10],<br>
           "min_samples_split": np.arange(2, 20, 2),<br>
           "min_samples_leaf": np.arange(1, 20, 2),<br>
           "max_features": [0.5, 1, "sqrt", "auto"],
           "max_samples": [10000]}

# Instantiate RandomizedSearchCV model
rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1, random_state=42),
                              param_distributions=rf_grid,
                              n_iter=2,
                              cv=5,
                              verbose=True)
# fit
rs_model.fit(X_train, y_train)`

Upvotes: 0

Venkatachalam
Venkatachalam

Reputation: 16966

If you want to create a dataframe for the results of each cv, use the following. Set return_train_score as True if you need the results for training dataset as well.

rf_random = RandomizedSearchCV(estimator = rf, return_train_score = True)
import pandas as pd
df = pd.DataFrame(rf_random.cv_results_)

Upvotes: 0

Tobi
Tobi

Reputation: 434

Add the 'scoring'-parameter to RandomizedSearchCV.

RandomizedSearchCV(scoring="neg_mean_squared_error", ...

Alternative options can be found in the docs

With this, you can print the RMSE for each parameter set, along with the parameter set:

cv_results = rf_random.cv_results_
for mean_score, params in zip(cv_results["mean_test_score"], cvres["params"]):
    print(np.sqrt(-mean_score), params)

Upvotes: 8

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