Machine Learning Fundamentals · Model evaluation

Train/test/validation split

Holding out data the model never trains on, so you can honestly check how it does on unseen examples.

Also known as: holdout set, data splitting.

Start a lesson on Train/test/validation split

Learn first

• Types of ML problems: The basic split between supervised (labeled examples), unsupervised (no labels), and reinforcement learning.

Leads to

• Cross-validation: Splitting the data multiple different ways and averaging performance, so one lucky/unlucky split doesn't mislead you.

Related topics

• Cross-validation: Splitting the data multiple different ways and averaging performance, so one lucky/unlucky split doesn't mislead you.