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Effective Amazon Machine Learning by Alexis Perrier

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Evaluating the performance of your model

Evaluating the predictive performance of a model requires defining a measure of the quality of its predictions. There are several available metrics both for regression and classification. The metrics used in the context of Amazon ML are the following ones:

  • RMSE for regression: The root mean squared error is defined by the square of the difference between the true outcome values and their predictions:
  • F-1 Score and ROC-AUC for classification: Amazon ML uses logistic regression for binary classification problems. For each prediction, logistic regression returns a value between 0 and 1. This value is ...

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