title: What n_estimators and max_features means in RandomForestRegressor
body: <p>I was reading about fine tuning the model using GridSearchCV and I came across a Parameter Grid Shown below :</p>
  
  <pre><code>param_grid = [
  {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},
  
  {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},
  ]
  forest_reg = RandomForestRegressor(random_state=42)
  # train across 5 folds, that's a total of (12+6)*5=90 rounds of training 
  grid_search = GridSearchCV(forest_reg, param_grid, cv=5,
                         scoring='neg_mean_squared_error')
  grid_search.fit(housing_prepared, housing_labels)
  </code></pre>
  
  <p>Here I am not getting the concept of n_estimator and max_feature. Is it like n_estimator means number of records from data and max_features means number of attributes to be selected from data? </p>
  
  <p>After Going further I got this result : </p>
  
  <pre><code>&gt;&gt; grid_search.best_params_
  {'max_feature':8, 'n_estimator':30}
  </code></pre>
  
  <p>So the thing is I am not getting what Actually this result want to say..</p>