Explore data, models, and algorithms through interactive visualizations.
See how AR and MA settings change a simulated time series and its autocorrelation patterns.
Use an interactive smoother to see how model flexibility changes underfitting, overfitting, train error, and test error.
Build synthetic classification data and watch a decision tree carve the feature space into regions.
Compare PCA, Isomap, t-SNE, and UMAP on simulated and real high-dimensional datasets.
Move through each K-means iteration to see centers, assignments, and convergence on generated clusters.
Apply K-means to image colors and compare the original image with a compressed color version.
Change noise, polynomial terms, and class structure to see how logistic regression shapes decision boundaries.
Adjust the Lorenz parameters and watch how small changes produce chaotic 3D trajectories.
Create matrices and compare how common decompositions reveal structure from different angles.