Overfitting, Underfitting, and Regularization

Let’s say you have a <a href="http://bit.ly/quaesita_emperorm" rel="noopener ugc nofollow" target="_blank">model</a> that is as good as you’re going to get for the information you have. To have an even better model, you need better <a href="http://bit.ly/quaesita_hist" rel="noopener ugc nofollow" target="_blank">data</a>. In other words, more data (quantity) or more relevant data (quality). When I say as good as you’re going to get, I mean in “good” terms of <a href="http://bit.ly/quaesita_msefav" rel="noopener ugc nofollow" target="_blank">MSE</a> performance on data your model hasn’t seen before. (It’s supposed to <a href="http://bit.ly/quaesita_parrot" rel="noopener ugc nofollow" target="_blank">predict</a>, not postdict.) You’ve done a perfect job of getting what you can from the information you have — the rest is error you can’t do anything about with your information. <a href="https://towardsdatascience.com/overfitting-underfitting-and-regularization-7f83dd998a62">Visit Now</a>