12.房价预测集成学习Stacking Learning

• 代码地址：appke/Los-House-Prices: 洛杉矶房价预测

import numpy as np 
import pandas as pd 

# 忽略警告信息
import warnings
warnings.filterwarnings("ignore")

数据集的准备

from sklearn.model_selection import train_test_split
train=pd.read_csv('datas/house_data.csv')
y=train['SalePrice']
train1=train.drop(['Id','SalePrice'],axis=1)
X=pd.get_dummies(train1).reset_index(drop=True)
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=123)

# tmp=train.isnull().sum()
# tmp[tmp>0] 

模型测评

from sklearn.metrics import mean_squared_error
def benchmark(model,testset,label):
    pred=model.predict(testset)
    if pred[pred<0].shape[0]>0:
        pri