Figure 4.

General ML pipeline for supervised learning: supervised predictive models are fed with features that are extracted and/or selected beforehand in an unsupervised way. Feature selection can, however, be embedded in some models, using regularization, for instance; selection then becomes supervised and therefore often improved. Classical (shallow) models tend to critically depend on unsupervised feature extraction and selection to preprocess data. In contrast, deep learning drops unsupervised feature extraction and selection; instead, it embeds multiple trainable layers of feature extractors and selectors, allowing the full pipeline to be supervised, end to end.