machine-learning

Estimation vocabulary for ML: bias, variance, and confidence

taught

Bias-vs-variability in an estimator and point-vs-interval estimates, restated as the vocabulary every model-evaluation chapter in this course reaches for.

Taught in

Estimation, bias, and confidence, for ML
Modern Machine Learning

Requires

bias-vs-variability-in-an-estimator required
point-vs-interval-estimates required

Required by

Does 90% confidence mean 90% correct? required
Models, training, inference, and prediction required
The bootstrap, for model evaluation required

Local knowledge map

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Solid arrows are required prerequisites; dashed are recommended or helpful. Lower rows are more foundational. Click any box to jump to that concept.