Scikit中的多元/多元线性回归了解吗?

问题描述

我在.csv文件中具有以下格式的数据集(dataTrain.csv和dataTest.csv):

I have a dataset (dataTrain.csv & dataTest.csv) in .csv file with this format:

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

并可以使用以下代码构建回归模型和预测:

And able to build a regression model and prediction with this code:

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，我想做的是多元回归.因此，模型将为CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)

However, what I want to do is multivariate regression. So, the model will be CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)

如何在scikit-learn中做到这一点?

How to do that in scikit-learn?

推荐答案

如果上面的代码适用于单变量，请尝试

If your code above works for univariate, try this

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]

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