Random forests

Machine Learning para Finanças em Python

Nathan George

Data Science Professor

ajuste polinomial

ajuste linear

Random forests

graphviz de random forest

Bootstrap aggregating (bagging)

bootstrap

Amostragem de atributos

Random Forests

Um conjunto (ensemble) de árvores de decisão
Bootstrap aggregating (bagging)
Amostragem de atributos a cada divisão

Implementação no sklearn

from sklearn.ensemble import RandomForestRegressor

random_forest = RandomForestRegressor()
random_forest.fit(train_features, train_targets)
print(random_forest.score(train_features, train_targets))

Hiperparâmetros

random_forest = RandomForestRegressor(n_estimators=200,
                                      max_depth=5,
                                      max_features=4,
                                      random_state=42)

ParameterGrid

from sklearn.model_selection import ParameterGrid

grid = {'n_estimators': [200], 
        'max_depth':[3, 5], 
        'max_features': [4, 8]}


from pprint import pprint
pprint(list(ParameterGrid(grid)))

[{'max_depth': 3, 'max_features': 4, 'n_estimators': 200},
 {'max_depth': 3, 'max_features': 8, 'n_estimators': 200},
 {'max_depth': 5, 'max_features': 4, 'n_estimators': 200},
 {'max_depth': 5, 'max_features': 8, 'n_estimators': 200}]

ParameterGrid

test_scores = []
# loop through the parameter grid, set hyperparameters, save the scores
for g in ParameterGrid(grid):
    rfr.set_params(**g)  # ** is "unpacking" the dictionary
    rfr.fit(train_features, train_targets)
    test_scores.append(rfr.score(test_features, test_targets))

# find best hyperparameters from the test score and print
best_idx = np.argmax(test_scores)
print(test_scores[best_idx])
print(ParameterGrid(grid)[best_idx])

0.05594252725411142
{'max_depth': 5, 'max_features': 8, 'n_estimators': 200}

Plante algumas random forests!

Machine Learning para Finanças em Python