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loss: <list[str]> (Optional)

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Description

The loss type to use during model optimization depending on the task type. Available Options:
Task TypeAvailable loss options
Binary Classification/Multilabel Classificationbinary_cross_entropy (default)
focal
Multiclass Classificationcross_entropy (default)
Regression/Forecastingmae
mse
huber (default)
multi_quantile
Temporal Link Predictioncross_entropy (default)
Static Link Predictioncross_entropy (default)
Multilabel Rankingcross_entropy (default)
By default, focal loss uses an alpha value of 0.25 (the weighting factor to balance positive vs. negative examples), and a gamma value of 2.0 (the balance between easy vs. hard examples). You can further customize this in the model plan by replacing the string by a dictionary: 
loss:
- name: focal
  alpha: 0.5
  gamma: 4.0
By default, huber loss uses a delta value of 1.0. You can further customize this in the model plan by replacing the string by a dictionary: 
loss:
- name: huber
  delta: 2.0
Use multi_quantile for regression or forecasting tasks when you want prediction intervals in addition to the median prediction. It trains multiple quantiles with pinball loss and writes TARGET_PRED together with 27 quantile columns named q_0.005, q_0.01, …, q_0.995: 
loss:
- multi_quantile
The full quantile column set is: 
q_0.005 q_0.01  q_0.02  q_0.025 q_0.05  q_0.1   q_0.15
q_0.2   q_0.25  q_0.3   q_0.35  q_0.4   q_0.45  q_0.5
q_0.55  q_0.6   q_0.65  q_0.7   q_0.75  q_0.8   q_0.85
q_0.9   q_0.95  q_0.975 q_0.98  q_0.99  q_0.995

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Supported Task Types

  • All