Foreste casuali

Machine Learning per la finanza in Python

Nathan George

Data Science Professor

adattamento polinomiale

adattamento lineare

Foreste casuali

graphviz della random forest

Bootstrap aggregating (bagging)

bootstrap

Campionamento delle feature

Random Forests

Un insieme (ensemble) di alberi decisionali
Bootstrap aggregating (bagging)
Campionamento di feature a ogni split

Implementazione con sklearn

from sklearn.ensemble import RandomForestRegressor

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

Iperparametri

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}

Pianta qualche random forest!

Machine Learning per la finanza in Python