sklearn线性回归系数具有单值输出

Question

我正在使用数据集来查看薪水与大学GPA之间的关系.我正在使用sklearn线性回归模型.我认为系数应该是截距和coff.相应特征的价值.但该模型给出了单一价值.

from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression

# Use only one feature : CollegeGPA
labour_data_gpa = labour_data[['collegeGPA']]

# salary as a dependent variable
labour_data_salary = labour_data[['Salary']]

# Split the data into training/testing sets
gpa_train, gpa_test, salary_train, salary_test = train_test_split(labour_data_gpa, labour_data_salary)

# Create linear regression object
 regression = LinearRegression()

# Train the model using the training sets (first parameter is x )
 regression.fit(gpa_train, salary_train)

#coefficients 
regression.coef_

The output is : Out[12]: array([[ 3235.66359637]])

Answer 1

尝试:

regression = LinearRegression(fit_intercept =True)
regression.fit(gpa_train, salary_train)

结果将在

regression.coef_
regression.intercept_

为了更好地理解线性回归,您可能应该考虑另一个模块,以下教程可以帮助:http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/ols.html