random_forest_classifier

def RandomForestFuction():
  rf = RandomForestClassifier(random_state = 42)
  n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
  max_features = ['auto', 'sqrt']      # Number of features to consider at every split
  max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]  # Maximum number of levels in tree
  max_depth.append(None)
  min_samples_split = [2, 5, 10]       # Minimum number of samples required to split a node
  min_samples_leaf = [1, 2, 4]         # Minimum number of samples required at each leaf node
  bootstrap = [True, False]            # Method of selecting samples for training each tree
  # Create the random grid
  random_grid = {'n_estimators': n_estimators,
                 'max_features': max_features,
                 'max_depth': max_depth,
                 'min_samples_split': min_samples_split,
                 'min_samples_leaf': min_samples_leaf,
                 'bootstrap': bootstrap}

  rf = RandomForestClassifier()       # Number of trees in random forest
  # 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)
  rf_random.fit(train, y_train)       # Fit the random search model
  print(rf_random.best_params_)       # Printing the params