Scikit Learn中的多元/多元线性回归？

Question

我有一个数据集（dataTrain.csv和dataTest.csv）以.csv文件的格式呈现：

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

能够使用此代码构建回归模型并进行预测：

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，我想要做的是多变量回归分析。因此，模型将为压缩因子(Z) = 截距 + 系数*温度(K) + 系数*压力(ATM)

如何在scikit-learn中实现？

- Drizzer Silverberg

---

只需在xtrain和xtest中包含温度和压力即可。 x_train = dataTrain[["Temperature(K)", "Pressure(ATM)"]]，然后对于x_test也是同样的操作。 - rtk22

Answer 1

如果您在单变量上的代码可行，请尝试这个

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]

- piRSquared

---

DataFrames没有reshape函数。要运行上面的代码，我必须首先使用values，例如x_train = dataTrain[['Temperature(K)', 'Pressure(ATM)']].values.reshape(-1,2)。 - Warren O'Neill

Answer 2

没错，你需要使用 .values.reshape(-1,2)

此外，如果你想知道表达式的系数和截距：

压缩因子(Z) = 截距 + 系数温度(K) + 系数压力(ATM)

你可以通过以下方式获取它们：

系数 = model.coef_
截距 = model.intercept_

- Fabrizio Peruzzo