I've built a few different linear regressions, using the same group of predictor variables, as you can see below:
model=LinearRegression()
model.fit(X=predictor_train,y=target_train)
prediction_train=model.predict(predictor_train)
pred=model.predict(main_frame.iloc[-1:,1:])
To create the predictions of the target variable, I suppose that the Scikit algorithm created an equation with those "predictor variables". My question is: How do I access that equation?
You're looking for params = model.coef_
. This returns an array with the weight of each model input.
Note that this is a linear equation, so to get the prediction for yourself, you want to form an equation such that your prediction, y = sum([input[i] * params[i]])
, if you had some input array called input
. This is the dot product, if you're familiar with linear algebra between the parameter vector and the feature vector.
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