Amazon Machine Learning
Developer Guide (Version Latest)

Step 4: Review the ML Model's Predictive Performance and Set a Score Threshold

Now that you've created your ML model and Amazon Machine Learning (Amazon ML) has evaluated it, let's see if it is good enough to put to use. During evaluation, Amazon ML computed an industry-standard quality metric, called the Area Under a Curve (AUC) metric, that expresses the performance quality of your ML model. Amazon ML also interprets the AUC metric to tell you if the quality of the ML model is adequate for most machine learning applications. (Learn more about AUC in Measuring ML Model Accuracy.) Let's review the AUC metric, and then adjust the score threshold or cut-off to optimize your model's predictive performance.

To review the AUC metric for your ML model

  1. On the ML model summary page, in the ML model report navigation pane, choose Evaluations, choose Evaluation: ML model: Banking model 1, and then choose Summary.

  2. On the Evaluation summary page, review the evaluation summary, including the model's AUC performance metric.

The ML model generates numeric prediction scores for each record in a prediction datasource, and then applies a threshold to convert these scores into binary labels of 0 (for no) or 1 (for yes). By changing the score threshold, you can adjust how the ML model assigns these labels. Now, set the score threshold.

To set a score threshold for your ML model

  1. On the Evaluation Summary page, choose Adjust Score Threshold.

    You can fine-tune your ML model performance metrics by adjusting the score threshold. Adjusting this value changes the level of confidence that the model must have in a prediction before it considers the prediction to be positive. It also changes how many false negatives and false positives you are willing to tolerate in your predictions.

    You can control the cutoff for what the model considers a positive prediction by increasing the score threshold until it considers only the predictions with the highest likelihood of being true positives as positive. You can also reduce the score threshold until you no longer have any false negatives. Choose your cutoff to reflect your business needs. For this tutorial, each false positive costs campaign money, so we want a high ratio of true positives to false positives.

  2. Let's say you want to target the top 3% of the customers that will subscribe to the product. Slide the vertical selector to set the score threshold to a value that corresponds to 3% of the records are predicted as "1".

    Note the impact of this score threshold on the ML model's performance: the false positive rate is 0.007. Let's assume that that false positive rate is acceptable.

  3. Choose Save score threshold at 0.77.

Every time you use this ML model to make predictions, it will predict records with scores over 0.77 as "1", and the rest of the records as "0".

To learn more about the score threshold, see Binary Classification.

Now you are ready to create predictions using your model.