Gradient Descent Dungeon
Playground
Mix algorithms and dataset shapes to observe model assumptions, decision surfaces, clustering behavior, and generalization.
This is not a pass/fail level. It is a sandbox for comparing what each algorithm can represent, where it struggles, and how dataset geometry changes the result.
Back to lessonsPlayground Setup
Choose an algorithm and dataset
K-Means Arena
Centroid assignment map
K-Means alternates between assigning each point to the nearest centroid and moving centroids toward the mean position of their assigned points.
K
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Inertia
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Shift
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Dataset Variant
Choose the data condition
Clean Blobs
mainThree compact groups. K-Means should work well with K = 3.
Training Console
Tune the model
Choose K, run assignment/update steps, and watch centroids move toward compact clusters.
K-Means idea
K-Means alternates between two simple steps: assign each point to the nearest centroid, then move each centroid to the average position of its assigned points.
Clustering Report
Train K-Means
Choose K, set the number of iterations, and train the model to inspect inertia, silhouette score, centroid movement, and cluster sizes.