Meaning of Feature Selection

Simple definition

Feature selection is the process of identifying and using only the most relevant attributes in a dataset to improve the performance and efficiency of machine learning models.

How to use Feature Selection in a professional context

Feature selection is used in high-dimensional datasets, such as genetics or text data, to reduce noise, enhance interpretability, and avoid overfitting in machine learning tasks.

Concrete example of Feature Selection

In a sentiment analysis model, only selecting features like “positive words count” and “negative words count” improves accuracy while ignoring less relevant features like word length.