sklearn 线性回归系数具有单值输出

Question

I am using dataset to see the relationship between salary and college GPA. I am using sklearn linear regression model. I think the coefficients should be intercept and the coff. value of corresponding feature. But the model is giving a single value.

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

Try:

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

and the results will be in

regression.coef_
regression.intercept_

In order to get a better understanding of your linear regression, you maybe should consider another module, the following tutorial helps: http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/ols.html

Answer 2

salary_pred = regression.predict(gpa_test)
print salary_pred
print salary_test

I think s alary_pred = regression.coef_*salary_test . Have a try that printed salary_pred and salary_test via pyplot. Figure can explain every thing.

Answer 3

Here you are training your model on a single feature gpa and a target salary :

regression.fit(gpa_train, salary_train)

If you train your model on multiple features eg python_gpa and java_gpa (with the target as salary ), then you would get two outputs signifying coefficients of the equation (for the linear regression model) and a single intercept.

It is equivalent to: ax + by + c = salary (where c is the intercept, a and b are the coefficients).