特徵重要度與梯度提升

Python 金融 Machine Learning

Nathan George

Data Science Professor

星期幾分裂

Python 金融 Machine Learning

200 日 SMA 分裂

Python 金融 Machine Learning

擷取特徵重要度

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]
Python 金融 Machine Learning

排序與繪圖

# 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()
Python 金融 Machine Learning

特徵重要度圖

Python 金融 Machine Learning

線性模型 vs 梯度提升

Camry 對上直升機

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

Python 金融 Machine Learning

提升示意圖

Python 金融 Machine Learning

提升式模型

可用的提升式模型：

  • Gradient boosting
  • Adaboost
Python 金融 Machine Learning

訓練梯度提升模型

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)
Python 金融 Machine Learning

開始提升吧！

Python 金融 Machine Learning

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