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.

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