Shallow Learning
Shallow learning refers to machine learning models with relatively simple architectures, typically involving one or a few layers of processing, such as traditional neural networks with limited depth. These models are often contrasted with deep learning approaches and include techniques like support vector machines, decision trees, and logistic regression. They are generally easier to train and interpret but may lack the capacity to learn complex hierarchical representations from data.
Developers should learn shallow learning when working on problems with limited data, requiring fast model training, or needing high interpretability, such as in credit scoring, medical diagnosis, or basic classification tasks. It is also useful as a baseline for comparing against more complex deep learning models, especially in domains where data is structured and feature engineering is straightforward.