天池学习赛：工业蒸汽量预测3——模型训练

接上一篇《天池学习赛：工业蒸汽量预测2——特征工程》

数据划分：

from sklearn.model_selection import train_test_split #切分数据

new_train_pca_16=new_train_pca_16.fillna(0)#采用PCA保留的16维特征的数据
train=new_train_pca_16[new_train_pca_16.columns]
target=new_train_pca_16['target']

#划分数据集   训练集80%验证机20%
train_data,test_data,train_target,test_target=train_test_split(train,target,\
                                                               test_size=0.2,random_state=0)

from sklearn.metrics import mean_squared_error  #评价指标

线性回归模型:

#从sklearn引入线性模型
from sklearn.linear_model import LinearRegression

clf=LinearRegression()
clf.fit(train_data,train_target)
test_pred=clf.predict(test_data)
score=mean_squared_error(test_target,clf.predict(test_data))
print("LinearRegression:  ",score)

k近邻回归模型:

from sklearn.neighbors import KNeighborsRegressor

clf=KNeighborsRegressor(n_neighbors=3)#最近的三个
clf.fit(train_data,train_target)
test_pred=clf.predict(test_data)
score=mean_squared_error(test_target,clf.predict(test_data))
print("KNeighborsRegressor:  ",score)

决策树回归模型:

from sklearn.tree import DecisionTreeRegressor

clf=DecisionTreeRegressor()
clf.fit(train_data,train_target)
test_pred=clf.predict(test_data)
score=mean_squared_error(test_target,clf.predict(test_data))
print("DecisionTreeRegressor:  ",score)

随机森林回归模型:

from sklearn.ensemble import RandomForestRegressor

clf=RandomForestRegressor(n_estimators=200)     #200树
clf.fit(train_data,train_target)
test_pred=clf.predict(test_data)
score=mean_squared_error(test_target,clf.predict(test_data))
print("RandomForestRegressor:  ",score)

LightGBM回归模型:

from lightgbm import LGBMRegressor

clf=LGBMRegressor(learning_rate=0.01,\
                  max_depth=-1,\
                  n_estimators=5000,\
                  boosting_type='gbdt',\
                  random_state=2019,\
                  objective='regression')
clf.fit(train_data,train_target)
test_pred=clf.predict(test_data)
score=mean_squared_error(test_target,clf.predict(test_data))
print("LGBMRegressor:  ",score)

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