Most machine learning algorithms tend to perform poorly as the number of dimensions in the data increases. This phenomenon is often known as the curse of dimensionality. Therefore, it is a good idea to reduce the number of features available in the data, while retaining the maximum amount of information possible. There are two ways to achieve this:
- Feature selection: This method involves identifying the features that have the least predictive power and dropping them altogether. Therefore, feature selection involves identifying a subset of features that is most important for that particular use case. An important distinction of feature selection is that it maintains the original meaning of every retained feature. For example, let's say we have a housing dataset with price, area, and number of rooms as features. Now, if we were to drop the area feature...