Hands-On Transfer Learning with Python

Feature selection

The process of feature extraction and engineering helps us extract as well as generate features from underlying datasets. There are cases where this leads to large inputs to an algorithm for processing. It such cases, it is suspected that many of the features in the input might be redundant and may lead to complex models and even overfitting. Feature selection is the process of identifying representative features from the complete feature set that is available/generated. The selected set of features are expected to contain the required information such that the algorithm is able to solve the given task without running into processing, complexity, and overfitting issues. Feature selection also helps in better understanding ...