simClassify+ Model Specifications

  • Updated

Model Specifications modify a model’s behavior and access to server resources.

simClassify+ accepts the same Sim Search K specification as simClassify, with the addition of several parameters for the blended metric. These hyperparameters cannot be effectively selected manually. Therefore, it is highly recommended to use a grid to select the appropriate parameters.


Number of iterations of the metric learning algorithm.

Learning Rate

Step size of the learning algorithm. Small values can lead to longer runtime. Large values can lead to overfitting.

Feature Subsampling

Ratio of randomly subsampled features at each iteration of the metric learning algorithm. Randomizations provides diversity in the preparation of the similarity criteria.

Feature Focus

Maximum number of dynamically selected features at any given time. This works like a localized feature selection process.

Class Weighting

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

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