Stochastic Gradient Descent

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

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

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

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

One complete traversal of the training dataset during training.

Halting training when validation performance stops improving to reduce overfitting.

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.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).