Feature Selection and Generalization using Regularization

Author(s): Shahriar Hossain

Originally published on Towards AI.

An Overview of L1 and L2 Regularization Techniques and a Case Study on Feature Selection Using Neural Networks
Source: Image by the author. The image was drawn using Canva.

Overfitting is a common challenge in neural network training, where the model learns the noise and details of the training data to an extent that negatively impacts its performance on new data. In this post, we will discuss overfitting, its implications, how to address it using L1 and L2 regularization, when to use L1 regularization and when to use L2 regularization, and finally, we’ll examine a case study demonstrating how L1 regularization can assist in identifying valuable features within the data.

This article is inspired by my video on overfitting and regularization, which you can watch here:

I made a minor mistake in the video by using an incorrect notation, but I corrected it in a two-minute erratum video linked below.

I also made sure to correct the error in this article, which is an extended version of the videos.

Overfitting occurs when a model is overly complex and starts memorizing the training data instead of learning to generalize from it. This usually happens when there are too many parameters compared to the number of observations. As a result, the model may perfectly fit the training data but might struggle to predict new, unseen… Read the full blog for free on Medium.

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Published via Towards AI