Calibration

A gradient method using random minibatches for efficient training on large datasets.

Controls the size of parameter updates; too high diverges, too low trains slowly or gets stuck.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

One complete traversal of the training dataset during training.

Of true negatives, the fraction correctly identified.

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.

Plots true positive rate vs false positive rate across thresholds; summarizes separability.

Scalar summary of ROC; measures ranking ability, not calibration.