【AI实战】xgb.XGBRegressor之多回归MultiOutputRegressor调参1

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已于 2022-08-24 08:14:48 修改 · 1.9k 阅读

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#人工智能 #XGBRegressor #多回归

于 2022-06-08 19:30:00 首次发布

本文介绍如何使用xgboost的XGBRegressor进行多目标预测，并通过GridSearchCV进行参数调优。作者分享了详细代码，包括数据加载、模型训练、参数搜索及评估指标，适合对xgboost在多变量回归中应用感兴趣的读者。

xgb.XGBRegressor之多回归MultiOutputRegressor调参1

环境
- Ubuntu18.04
- python3.6.9
- xgboost 1.5.2

依赖库

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV #网格搜索
from sklearn.metrics import make_scorer
from sklearn.metrics import r2_score
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
import joblib

szZack的博客

调参核心代码

def tune_parameter(train_data_path, test_data_path, n_input, n_output, version):
    # 模型调参
    
    x,y = load_data(version, 'train', train_data_path, n_input, n_output)
    train_x,test_x,train_y,test_y = train_test_split(x,y,test_size=0.2,random_state=2022)
    
    gsc