In order to avoid overfitting in regression due to too many feature while at the same time have enough features to minimize the sum of squared errors in order to get a more accurate fit on the test data, you need to regularize the regression.<\/p>\n
This can be done with a Lasso regression where you want to minimize the sim of squared errors + plus a penalty parameters times the\u00a0coefficient of the regression (which indicates the amount of features)<\/p>\n
minimize SSE +\u00a0\u03bb|\u03b2|<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
In order to avoid overfitting in regression due to too many feature while at the same time have enough features to minimize the sum of squared errors in order to get a more accurate fit on the test data, you need to regularize the regression. This can be done with a Lasso regression where you … <\/p>\n