Using Autoencoders for Feature Selection

This post introduces autoencoders as a method for feature selection in machine learning. It explains the architecture of autoencoders (encoder, latent space, decoder) and their application in dimensionality reduction and feature extraction. The post contrasts autoencoders with traditional feature selection methods, highlighting their ability to handle non-linear relationships and automate feature engineering. A practical example using the Iris dataset demonstrates the steps involved: data preprocessing, building an autoencoder with Keras, and using the latent space representation for feature selection.