Random Forest high R2 Score but poor prediction

[donnertrud]

Thanks for your response !
Instead of coding a loop which goes through different estimators, couldn't I run a RandomSearch with ranges for relevant RF parameters and looking for the best one with the best.paras_ command? Because I already did that a couple of times now and the R2 and MAE got even worse, surprisingly. I might have to drop some features and try the random serch again.

e.g. the code would look something 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, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)
search = rf_random.fit(X_train, y_train)
search.best_params_