title: Random Forest tuning with RandomizedSearchCV
body: <p>I have a few questions concerning Randomized grid search in a Random Forest Regression Model. My parameter grid looks like this:</p>
  
  <pre><code>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]}
  </code></pre>
  
  <p>and my code for the RandomizedSearchCV like this:</p>
  
  <pre><code># 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)
  </code></pre>
  
  <p>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?</p>
  
  <pre><code>rf_random.best_params_
  rf_random.best_score_
  rf_random.best_estimator_
  </code></pre>
  
  <p>thank you,
  R</p>