Jan-16-2020, 03:02 PM

Hello,

I have been trying to optimize the Random Forest Hyperparameter in order to lower the Mean Absolute Error of my Regression Model. I used Python to automatically look for those best parameters, which were the following :

(n_estimators= , bootstrap=, min_samples_split=, min_samples_leaf=, max_features=", max_depth=). The code looked like this :

I have been trying to optimize the Random Forest Hyperparameter in order to lower the Mean Absolute Error of my Regression Model. I used Python to automatically look for those best parameters, which were the following :

(n_estimators= , bootstrap=, min_samples_split=, min_samples_leaf=, max_features=", max_depth=). The code looked like this :

# Number of trees in random forest n_estimators = [int(x) for x in np.linspace(start = 200, stop = 5000)] # Number of features to consider at every split max_features = ['auto', 'sqrt', 'log2'] # Maximum number of levels in tree max_depth = [int(x) for x in np.linspace(10, 110)] max_depth.append(None) # Minimum number of samples required to split a node min_samples_split = [2, 5, 10, 15, 20] # Minimum number of samples required at each leaf node min_samples_leaf = [1, 2, 5, 10, 15] # Method of selecting samples for training each tree bootstrap = [True, False]# 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_random = RandomizedSearchCV(estimator = rf, scoring="neg_mean_absolute_error", param_distributions = random_grid, cv= 3, n_iter = 100, verbose=2, random_state=42, n_jobs = -1) search = rf_random.fit(X_train, y_train) print("best parameters for MAE: ", search.best_params_)Eventually I used the values that the machine gave me and plugged them in, however, the MAE is worse than if I simply put in values by trial and error. I wonder how that is possible, or in other words what am I doing wrong ? How can trial and error yield better? thanks in advance !