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
...,...,...
并能用此代码建立回归模型和预测：
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]
然而，我想做的是多元回归。因此，模型将为
CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)如何在Scikit中学习？
最佳答案
如果上面的代码适用于单变量，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)']].reshape(-1,2)
y_train = dataTrain['CompressibilityFactor(Z)']
x_test = dataTest[['Temperature(K)', 'Pressure(ATM)']].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]