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. |
Supervised Recipe Specifications
- Updated
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