二手车价格预测——Task5 模型融合

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前言

当我们在做数据挖掘的时候，往往会发现单个模型的预测结果总是不如人意，这个时候我们不妨尝试模型融合，把多个模型通过加权融合或stacking融合的方法进行融合.往往这样我们能够得到更好的效果. 详细的stacking理论可以参考以下链接: [link](https://blog.youkuaiyun.com/wstcjf/article/details/77989963) 或者是参考以下链接： https://blog.youkuaiyun.com/Bossun1127/article/details/105286165

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一、代码示例

1.引入库

代码如下（示例）：

import pandas as pd
import numpy as np
import warnings
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns

warnings.filterwarnings('ignore')
%matplotlib inline

import itertools
import matplotlib.gridspec as gridspec
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
# from mlxtend.classifier import StackingClassifier
from sklearn.model_selection import cross_val_score, train_test_split
# from mlxtend.plotting import plot_learning_curves
# from mlxtend.plotting import plot_decision_regions

from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split

from sklearn import linear_model
from sklearn import preprocessing
from sklearn.svm import SVR
from sklearn.decomposition import PCA,FastICA,FactorAnalysis,SparsePCA

import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import GridSearchCV,cross_val_score
from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor

from sklearn.metrics import mean_squared_error, mean_absolute_error

2.读入数据

代码如下（示例）：

Train_data = pd.read_csv('C:\\Users\\TINKPAD\\Desktop\\python_work\\kaggle\二手车交易价格预测\\used_car_train_20200313.csv', sep=' ')
TestA_data = pd.read_csv('C:\\Users\\TINKPAD\\Desktop\\python_work\\kaggle\二手车交易价格预测\\used_car_testB_20200421.csv', sep=' ')

print(Train_data.shape)
print(TestA_data.shape)

(150000, 31)
(50000, 30)

numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)

feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','price']]

X_data = Train_data[feature_cols]
Y_data = Train_data['price']

X_test  = TestA_data[feature_cols]

print('X train shape:',X_data.shape)
print('X test shape:',X_test.shape)

Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
       'gearbox', 'power', 'kilometer', 'regionCode', 'seller', 'offerType',
       'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6',
       'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14'],
      dtype='object')
X train shape: (150000, 26)
X test shape: (50000, 26)

def Sta_inf(data):
    print('_min',np.min(data))
    print('_max:',np.