Supervised Recipe Specifications

Learning Rate

Step size of metric learning algorithm. Small values can lead to longer run times and large values can lead to overfitting.

Threshold

For binary classification, the minimum probability for a prediction to be considered as true.

Class Column

The dataset column to use as the class.

Feature Subsampling

Ratio of randomly subsampled features in each iteration of the metric learning algorithm. Randomization provides diversity in the resulting similarity metric.

Class Weighting

UNIFORM gives the same weight to all classes. NORMALIZED takes into account class imbalance.

Iterations

Number of iterations of the metric learning algorithm.