Machine Learning with Spark by Nick Pentreath

Author:Nick Pentreath [Pentreath, Nick]
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2015-02-20T05:00:00+00:00

Your output should look like this:

0.001 L2 regularization parameter, AUC = 66.55% 0.01 L2 regularization parameter, AUC = 66.55% 0.1 L2 regularization parameter, AUC = 66.63% 1.0 L2 regularization parameter, AUC = 66.04% 10.0 L2 regularization parameter, AUC = 35.33%

As we can see, at low levels of regularization, there is not much impact in model performance. However, as we increase regularization, we can see the impact of under-fitting on our model evaluation.

Tip

You will find similar results when using the L1 regularization. Give it a try by performing the same evaluation of regularization parameter against the AUC measure for L1Updater.

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