Labs

Two continuous features, one binary label. Fit a classifier and score accuracy on held-out rows; a majority-class guess scores 0.60.

Classify messages from six keyword counts. F1 is measured for the spam class, and predicting every message as ham scores 0.00.

Estimate sale price from size, bedrooms, and age. Lower is better: predicting the training mean scores an MSE of 2879.57.

The target follows a noisy quadratic in one feature. Explain the variance on held-out data; a straight-line fit only reaches R-squared 0.26.

Four well-separated groups in feature space, each with its own target level. Cluster the training rows and predict a value per test row; predicting the global mean scores R-squared 0.00.

Only about a third of applicants default. F1 is measured for the default class, and predicting everyone as safe scores 0.00.

Two sensor readings predict a target, but a few training rows are corrupted by outliers. Lower is better: the training-mean baseline scores an MSE of 84.91.

An XOR label over two features: no linear boundary beats 0.75 accuracy. Reach 0.90 on held-out rows with an engineered interaction.