Scikit中的多变量/多元线性回归学习？

Question

我在.csv文件中有一个数据集(dataTrain.csv和dataTest.csv),格式如下:

Temperature(K),Pressure(ATM),CompressibilityFactor(Z)
273.1,24.675,0.806677258
313.1,24.675,0.888394713
...,...,...

并且能够使用以下代码构建回归模型和预测:

import pandas as pd
from sklearn import linear_model

dataTrain = pd.read_csv("dataTrain.csv")
dataTest = pd.read_csv("dataTest.csv")
# print df.head()

x_train = dataTrain['Temperature(K)'].reshape(-1,1)
y_train = dataTrain['CompressibilityFactor(Z)']

x_test = dataTest['Temperature(K)'].reshape(-1,1)
y_test = dataTest['CompressibilityFactor(Z)']

ols = linear_model.LinearRegression()
model = ols.fit(x_train, y_train)

print model.predict(x_test)[0:5]

但是,我想要做的是多元回归.所以,模型将是CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)

如何在scikit-learn中做到这一点？

Answer 1

如果您的上述代码适用于单变量,请尝试此操作

import pandas as pd
from sklearn import linear_model

dataTrain = pd.read_csv("dataTrain.csv")
dataTest = pd.read_csv("dataTest.csv")
# print df.head()

x_train = dataTrain[['Temperature(K)', 'Pressure(ATM)']].to_numpy().reshape(-1,2)
y_train = dataTrain['CompressibilityFactor(Z)']

x_test = dataTest[['Temperature(K)', 'Pressure(ATM)']].to_numpy().reshape(-1,2)
y_test = dataTest['CompressibilityFactor(Z)']

ols = linear_model.LinearRegression()
model = ols.fit(x_train, y_train)

print model.predict(x_test)[0:5]