A new interactive publication
Machine learning,
made tangible.
Visual essays and small experiments for understanding what models are really doing — one difficult idea at a time.
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8 articles
Beyond CART: Decision Trees Don't Have to Look Like CART
Identify the four architectural assumptions behind CART, then reason about what oblique, soft, oblivious, and model trees gain and give up by changing them.
Gradient boosted decision treesGradient Boosting: A Quick Deep Dive
Start from one constant prediction, fit small trees to the corrections that remain, and discover how a chosen loss turns boosting into gradient descent in function space.
Bagging and model varianceWhy averaging trees makes predictions more stable
Keep one input fixed, watch tree predictions move as the data changes, and use averaging to narrow their distribution.
Sample-based reinforcement learningA Sample-Based View of Reinforcement Learning
Follow experience through an MDP, then compare how reinforcement learning algorithms collect samples, update learned quantities, and represent what they learn.
ACF and PACFHow the Past Echoes: Understanding ACF and PACF
Begin with ordinary correlation, shift a series through time, and see how lagged relationships become an autocorrelation function.
Reward hackingReward Hacking in Reinforcement Learning
Three small MDPs show how an agent can exploit a missing restriction, manipulate a measurement process, or optimize an incomplete objective.
Regression treesHow decision trees learn to predict numbers
Keep the same propose–judge–repeat procedure, replace class impurity with squared error, and let each leaf predict its mean.
Decision treesHow to invent the decision tree algorithm from scratch
Build a classification tree by hand from ten seeds, then automate your own judgment with midpoint thresholds and the Gini score.