The Ultimate Guide to Feature Engineering: Feature Selection Hacks

Master feature engineering and selection to build powerful machine learning models. Learn top techniques to transform raw data into high-impact insights!

Machine learning models are only as good as the data they’re trained on. It’s not just about how complex an algorithm is—the real power comes from using the right features.

This is where Feature Engineering and Feature Selection come into the show. In our last article, I went through all the different types of evaluation metrics. But how can we further a models performance?

Feature Engineering is all about creating, changing, and improving features to make them more useful for predictions. Feature Selection is about removing unnecessary or repetitive features that could hurt a model’s performance.

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When used together, these techniques help make our models more accurate, easier to understand, and more efficient.

So the goal of this article is to build on supervised learning and introduce you to these techniques to help boost your models performance in the long run.

By the time you finish this article, you’ll have practical skills to improve your machine learning process through the engineering and selection of features!

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Time to get featuring engineering and improving some models!