Random Forest tuning with RandomizedSearchCV
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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)
Author by
raffa_sa
Updated on September 16, 2022Comments
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raffa_sa over 1 year
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
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raffa_sa over 5 yearsso the RandomizedSearchCV should now internally work with the RMSE right? Then i don't understand my result. I get for
rf_random.best_score_
this result-13684.3
. RMSE can't be negative normally? @Tobi -
Tobi over 5 yearsYou are almost correct. It is working with the MSE (without the Square). However, for Grid/Randomized/...SearchCV it has to be the negative MSE. And that is why I used np.sqrt( - mean_score). An explanation for the negation is given here: stackoverflow.com/questions/21050110/….