Grid Search for elastic net regression
This report contains the run for the elastic net regression using sklearn. The parameters that we are tuning are alpha and l1_ratio. We have the following alpha and l1_ratio search space.
alpha_list=[0.001, 0.01, 0.1, 1, 10]
l1_ratio_list = np.arange(0.0,1.0,0.1).tolist()
Created on February 13|Last edited on February 13
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Section 1
This set of panels contains runs from a private project, which cannot be shown in this report
- This is the run for different alpha and l1_ratio. We can see that the alpha value of 0.001, and l1_ratio of 0.0 gives the best train and validation MSE. Hence, the ridge regression turns out to be the better model.
This set of panels contains runs from a private project, which cannot be shown in this report
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