Pentingnya fitur dan gradient boosting

Machine Learning untuk Keuangan dengan Python

Nathan George

Data Science Professor

pemisahan hari dalam minggu

pemisahan SMA 200 hari

Mengekstrak pentingnya fitur

from sklearn.ensemble import RandomForestRegressor

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

feature_importances = random_forest.feature_importances_

print(feature_importances)

[0.07586547 0.10697602 0.12215955 0.23969227 0.29010304 0.0314028
 0.11977058 0.00276721 0.00246329 0.0026431  0.00615667]

Mengurutkan dan memplot

# feature importances from random forest model
importances = random_forest.feature_importances_

# index of greatest to least feature importances
sorted_index = np.argsort(importances)[::-1]

x = range(len(importances))
# create tick labels
labels = np.array(feature_names)[sorted_index]

plt.bar(x, importances[sorted_index], tick_label=labels)

# rotate tick labels to vertical
plt.xticks(rotation=90)
plt.show()

plot pentingnya fitur

Model linear vs gradient boosting

camry vs helikopter

https://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/

diagram boosting

Model boosted

Model boosted yang tersedia:

Gradient boosting
Adaboost

Melatih model gradient boosting

from sklearn.ensemble import GradientBoostingRegressor

gbr = GradientBoostingRegressor(max_features=4,
                                learning_rate=0.01,
                                n_estimators=200,
                                subsample=0.6,
                                random_state=42)

gbr.fit(train_features, train_targets)

Get boosted!

Machine Learning untuk Keuangan dengan Python