Task 1.2模型构建之集成模型

1. 目标：构建随机森林、GBDT、XGBoost和LightGBM这4个模型，并对每一个模型进行评分，评分方式任意，例如准确度和auc值。
2. 构建随机森林并评分

#构建随机森林

#导入工具包
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandarScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier

#读取。
data = pd.read_csv(\"data_all.csv\")
x = data.drop(labels = \'status\', axis = 1)
y = data[\'status\']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_size = 2018)

#数据标准化
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test) 

#建立模型，并进行模型训练。
rfc = RandomForestClassifier()
rfc.fit(x_train,y_train)
rfc_score = rfc.score(x_test, y_test)

#打印
print(rfc_score)

1. 构建GBDT并评分

#构建GBDT算法

#导入工具包
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandarScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier

#读取。
data = pd.read_csv(\"data_all.csv\")
x = data.drop(labels = \'status\', axis = 1)
y = data[\'status\']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_size = 2018)

#数据标准化
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test) 
#模型训练，使用GBDT算法
gbdt = GradientBoostingRegressor()
gbdt.fit(x_train, y_train
gdbt_score = gdbt.score(x_test, y_test)

#打印
print(gdbt_score)

1. 构建XDBoost并评分

#构建XGBoost模型

#导入工具包
import pandas as pd
import xgboost as xgb 
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandarScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier

#读取
data = pd.read_csv(\"data_all.csv\")
x = data.drop(labels = \'status\', axis = 1)
y = data[\'status\']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_size = 2018)

#数据标准化
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test) 

#XDBoost模型
xgb = xgb.XGBClassifier()
xbg.fit(x_traim, y_train)
xgb_score = xgb.score(x_test, y_test)

#打印
print(xgb_score)

4 构建lightGBM并评分

#构建lightGBM模型

#导入工具包
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandarScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier

#读取
data = pd.read_csv(\"data_all.csv\")
x = data.drop(labels = \'status\', axis = 1)
y = data[\'status\']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_size = 2018)

#数据标准化
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test) 

#lightGBM模型
gbm = lgb.LGBMRegressor()
gbm.fit(x_train, y_train)
gbm_score = gbm.score(x_test, y_test)

#打印
print(gbm_score)