Tinh chỉnh siêu tham số xgboost trong pipeline

Gradient Boosting Cực Mạnh với XGBoost

Sergey Fogelson

Head of Data Science, TelevisaUnivision

Tinh chỉnh siêu tham số XGBoost trong pipeline

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 RandomizedSearchCV

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())]

gbm_param_grid = {
   ...:     'xgb_model__subsample': np.arange(.05, 1, .05),
   ...:     'xgb_model__max_depth': np.arange(3,20,1),
   ...:     'xgb_model__colsample_bytree': np.arange(.1,1.05,.05) }

randomized_neg_mse = RandomizedSearchCV(estimator=xgb_pipeline,
   ...: param_distributions=gbm_param_grid, n_iter=10,
   ...: scoring='neg_mean_squared_error', cv=4)

randomized_neg_mse.fit(X, y)

Tinh chỉnh siêu tham số XGBoost trong pipeline II

print("Best rmse: ", np.sqrt(np.abs(randomized_neg_mse.best_score_)))

RMSE tốt nhất: 3.9966784203040677

print("Best model: ", randomized_neg_mse.best_estimator_)

Mô hình tốt nhất:  Pipeline(steps=[('st_scaler', StandardScaler(copy=True, 
with_mean=True, with_std=True)),
('xgb_model', XGBRegressor(base_score=0.5, colsample_bylevel=1,
       colsample_bytree=0.95000000000000029, gamma=0, learning_rate=0.1,
       max_delta_step=0, max_depth=8, min_child_weight=1, missing=None,
       n_estimators=100, nthread=-1, objective='reg:squarederror', reg_alpha=0,
       reg_lambda=1, scale_pos_weight=1, seed=0, silent=True,
       subsample=0.90000000000000013))])

Hãy hoàn tất nào!

Gradient Boosting Cực Mạnh với XGBoost