我在.csv文件中有一个数据集(dataTrain.csv& dataTest.csv),格式为:
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。因此,模型将为CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)
如何在scikit-learn中做到这一点?
答案 0 :(得分:17)
如果您的上述代码适用于单变量,请尝试使用
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]
答案 1 :(得分:0)
那是正确的,您需要使用.values.reshape(-1,2)
此外,如果您想知道表达式的系数和截距:
CompressibilityFactor(Z)=截距+ coef 温度(K)+ coef 压力(ATM)
您可以通过以下方式获得它们:
系数= model.coef_
拦截= model.intercept _