梯度提升（GB）

Machine Learning with Tree-Based Models in Python

Elie Kawerk

Data Scientist

梯度提升樹

逐步修正前一模型的錯誤。
不會調整訓練樣本的權重。
每個預測器以前一個的殘差當作標籤來訓練。
梯度提升樹：以 CART 為基學習器。

回歸的梯度提升樹：訓練

GBT-train

縮減（Shrinkage）

GBT-lr

梯度提升樹：預測

回歸：
- $y_{pred} = y_1 + \eta r_1 + ... + \eta r_N$
- 在 sklearn 中：GradientBoostingRegressor。
分類：
- 在 sklearn 中：GradientBoostingClassifier。

sklearn 的梯度提升（auto 資料集）

# Import models and utility functions
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error as MSE

# Set seed for reproducibility
SEED = 1

# Split dataset into 70% train and 30% test
X_train, X_test, y_train, y_test = train_test_split(X,y, 
                                                    test_size=0.3, 
                                                    random_state=SEED)

# Instantiate a GradientBoostingRegressor 'gbt'
gbt = GradientBoostingRegressor(n_estimators=300, max_depth=1, random_state=SEED)


# Fit 'gbt' to the training set
gbt.fit(X_train, y_train)

# Predict the test set labels
y_pred = gbt.predict(X_test)

# Evaluate the test set RMSE
rmse_test = MSE(y_test, y_pred)**(1/2)

# Print the test set RMSE
print('Test set RMSE: {:.2f}'.format(rmse_test))

Test set RMSE: 4.01

一起來練習吧！

Machine Learning with Tree-Based Models in Python