Visual Explainers

How we find the rare exception in a sea of normal data.

Two ensemble strategies, two different problems: bagging trains parallel trees on random samples to squash variance; boosting chains sequential trees to fix bias one residual at a time.

How we find natural groupings in unlabelled data.

Carve the data space into rectangles with a series of yes-or-no questions, predicting a single value for everyone in the same box.

How to squash high-dimensional data into a human-readable 2D map.

Chain weak learners so each one corrects only the mistakes of the one before it — an additive process that converts many shallow trees into a powerful ensemble.

Five pictures: stand on the loss surface, read the uphill arrow, flip it, take small steps, and see where the flat stretches fool you.

Predict a new point's class by looking at the k training points closest to it and taking a majority vote.

Fit a straight line through scattered data points by minimising the vertical errors, giving a single trend for prediction.

Bend a straight line into an S-curve to predict probabilities for yes-or-no categories.

Train hundreds of trees on random slices of data, let them vote, and watch individual errors cancel each other out — leaving a model that generalises far better than any single tree.