Linear Learner - Multiclass classification - Prediction Scores

Question

Hi,  
I am using the linear learner algo for multiclass classification with 5 classes and 9 features  
  
I'm trying to figure out what the scores relate to? After getting the predicted_label result I get 5 scores....eg....  
  
{"score": \[0.0003054474655073136, 0.8110334873199463, 0.0521857813000679, 0.1320497989654541, 0.004425559192895889], "predicted_label": 1.0}  
  
or  
  
{"score": \[0.0001415203878423199, 0.11968196928501129, 0.8732749223709106, 0.006143826059997082, 0.0007577905780635774], "predicted_label": 2.0}  
  
Does each score relate to a class? I have tried searching the internet for an explanation but so far cannot find anything, hopefully someone can enlighten me  
  
Regards

AWS-User-7153803 · Answer

Thanks for using SageMaker. The scores returned by multiclass models are the predicted probabilites for each class.   
  
In your first example, the model is predicting class 1 with 81% confidence, and in your second example, the model is predicting class 2 with 87% confidence.  
  
In general, to interpret the `score` from classification models, you have to consider the loss function. If the loss is `logistic` for binary classification or `softmax_loss` for multiclass classification, then the `score` can be interpreted as a probability. These are the default losses used by Linear Learner. But if the loss is `hinge_loss`, then the `score` cannot be interpreted as a probability. This is because hinge loss corresponds to a Support Vector Classifier, which does not produce probability estimates.

badders84 · Answer

Great, many thanks for the explanation

Linear Learner - Multiclass classification - Prediction Scores

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