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What are features in ML?

Explained in one sentence, features or variables are individual properties generated from a data set and used as input to ML models. Overall, we represent features as numerical columns in data sets, but they can also be strings.

Why are features important in ML modeling?

Features are one of the most important components in ML modeling. Basically, model predictions directly depend on the quality of features. As a result, in ML, a special emphasis is placed on feature engineering and feature selection.

Why is feature importance important in ML?

Also, feature importance is commonly used as a tool for ML model interpretability. From the scores, it’s possible to explain why the ML model makes particular predictions and how we can manipulate features to change its predictions. There are many ways of calculating feature importance, but generally, we can divide them into two groups:

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