Why having many features can hinder your model’s performance

The activity of feature engineering can be very useful for improving the performance of a predictive model. However, it could worsen our results if we don’t keep in mind certain principles to avoid.

A data analysis project always begins with a dataset. This may have been delivered by the customer, found publicly on sites like Kaggle.com, or created by us and our team.

In any of these cases, the dataset will show an anatomy that will vary …

common-mistakes data science feature engineering features machine learning performance tips-and-tricks