Model selection with validation, cross validation and regularization. Polynomial regression
<p>This workflow is designed to illustrate the process of model selection. The considered models are polynomials of degree 1 to 10.</p><p>It is shown that training error always decreases with degree (i.e., with model flexibility). Initially, test error also decreases with degree but, in stark contrast with training error, test error ends up increasing (overfitting). This is shown estimating the test error both with validation and cross validation.</p><p>Finally, we illustrate how you can use regularization to reduce overfitting.</p>
This workflow is designed to illustrate the process of model selection. The considered models are polynomials of degree 1 to 10.
It is shown that training error always decreases with degree (i.e., with model flexibility). Initially, test error also decreases with degree but, in stark contrast with training error, test error ends up increasing (overfitting). This is shown estimating the test error both with validation and cross validation.
Finally, we illustrate how you can use regularization to reduce overfitting.
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