將 xgboost 納入 pipeline

使用 XGBoost 的極端梯度提升

Sergey Fogelson

Head of Data Science, TelevisaUnivision

使用 XGBoost 的 scikit-learn pipeline 範例

import pandas as pd
import xgboost as xgb
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score

names = ["crime","zone","industry","charles","no","rooms","age", 
        "distance","radial","tax","pupil","aam","lower","med_price"]
data = pd.read_csv("boston_housing.csv",names=names)
X, y = data.iloc[:,:-1], data.iloc[:,-1]

xgb_pipeline = Pipeline[("st_scaler", StandardScaler()),
                        ("xgb_model",xgb.XGBRegressor())]
scores = cross_val_score(xgb_pipeline, X, y, 
                         scoring="neg_mean_squared_error",cv=10)

final_avg_rmse = np.mean(np.sqrt(np.abs(scores)))
print("Final XGB RMSE:", final_avg_rmse)

Final RMSE: 4.02719593323

pipeline 新增元件介紹

sklearn_pandas：
- DataFrameMapper－在 pandas 與 scikit-learn 之間的互通
sklearn.impute：
- SimpleImputer－在 scikit-learn 中原生補齊數值與類別欄位
sklearn.pipeline：
- FeatureUnion－將多個特徵的 pipeline 合併為單一特徵 pipeline

一起來練習吧！

使用 XGBoost 的極端梯度提升