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weight_mode: (sample | weighted_loss) (Optional)

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Description

weight_mode specifies how Kumo applies training weights when either a training-table WEIGHT column is present and/or majority_sampling_ratio is configured for binary classification. The training-table WEIGHT column can come from either:
  • SDK/custom training-table workflows (see How do I assign different weights to training samples?)
  • generated weights via weight_col in training_table_generation
  • sample: samples training examples with replacement according to the effective weight distribution.
  • weighted_loss: weighs training examples in the loss function according to the effective weight distribution.
When both a training-table weight column and majority_sampling_ratio are set for binary classification:
  • In sample mode, row and class weights are combined for sampling.
  • In weighted_loss mode, row and class weights are combined in loss weighting.

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Supported Task Types

  • All

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Default Values

When weight_col is configured, Kumo defaults to weighted_loss — the AutoML plan sets weight_mode: [weighted_loss] automatically, so the loss function is weighted by the column values without further AutoML search. When only majority_sampling_ratio is set for binary classification (no weight_col), AutoML does not set a default weight_mode and you must choose one explicitly.