◐ learning · kind concept · level 2 · 9h
- Aplica: Feature Engineering
Keeping the features that carry signal and dropping the rest — filter, wrapper, and embedded methods. Fewer, better features means less overfitting and faster models.
In machine learning, feature selection is the process of selecting a subset of relevant features for use in model construction. Feature selection techniques are used for several reasons:simplification of models to make them easier to interpret, shorter training times, to avoid the curse of dimensionality, improve the compatibility of the data with a certain learning model class, to encode inherent symmetries present in the input space.
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- Aplica: Feature Engineering