Feature Selection and Removing in Machine Learning

Author(s): Amit Chauhan

Originally published on Towards AI.

Improving model and its accuracy for high dimension data

This member-only story is on us. Upgrade to access all of Medium.

Photo by Franki Chamaki on Unsplash

As we know the importance of features in the machine learning algorithms are playing a very crucial role in prediction analysis in any field.

When the data features become very complex then there are very high chances to get a multi-collinearity situation or high correlation between two and more features. This situation strikes badly on the training of data and it might go over-fitting or under-fitting of the data.

There are some methods to select and remove features as shown below:

Feature Selection Methods1. Uni-variate Selection2. Selecting… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI