Algorithm Selector

Work from your problem type and data characteristics to a shortlist of algorithms, with the trade-off that decided each branch stated in the open.

It covers supervised and unsupervised learning, classification, regression, ranking, forecasting, anomaly detection and the semi-supervised middle ground. It is written for data scientists choosing a first model, and engineers sanity-checking one someone else chose.

Every branch states the trade-off that decided it, so the recommendation you end on comes with the reasoning attached — something you can paste into a design note or defend in a review.