Topic
Machine learning
Machine learning uses data to fit models that perform tasks such as predicting categories or values.
At a glance
- Supervised examples
- Classification and regression
- Evaluation boundary
- Data separate from model fitting
Overview
Machine learning uses data to fit models that perform tasks such as predicting categories or values. The training process selects model parameters from examples, while evaluation tests whether the resulting behavior is useful on data beyond the examples used for fitting.
A complete workflow
Data preparation, model fitting, and evaluation form connected parts of a workflow. Preprocessing can also learn from data, so it belongs inside the training boundary when measuring generalization.
Measuring useful behavior
Choose metrics around the actual task and compare against a sensible baseline. A single score does not describe every failure mode or establish that the data represents the intended deployment environment.
Sources and review
MOOR's explanatory text is supported by the following source links.
- Getting started — scikit-learn
- Common pitfalls — scikit-learn