Evaluation Alerts - Amazon Machine Learning

We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.

Evaluation Alerts

Amazon ML provides insights to help you validate whether you evaluated the model correctly. If any of the validation criteria are not met by the evaluation, the Amazon ML console alerts you by displaying the validation criterion that has been violated, as follows.

  • Evaluation of ML model is done on held-out data

    Amazon ML alerts you if you use the same datasource for training and evaluation. If you use Amazon ML to split your data, you will meet this validity criterion. If you do not use Amazon ML to split your data, make sure to evaluate your ML model with a datasource other than the training datasource.

  • Sufficient data was used for the evaluation of the predictive model

    Amazon ML alerts you if the number of observations/records in your evaluation data is less than 10% the number of observations you have in your training datasource. To properly evaluate your model, it is important to provide a sufficiently large data sample. This criterion provides a check to let you know if you are using too little data. The amount of data required to evaluate your ML model is subjective. 10% is selected here as a stop gap in the absence of a better measure.

  • Schema matched

    Amazon ML alerts you if the schema for the training and evaluation datasource are not the same. If you have certain attributes that do not exist in the evaluation datasource or if you have additional attributes, Amazon ML displays this alert.

  • All records from evaluation files were used for predictive model performance evaluation

    It is important to know if all the records provided for evaluation were actually used for evaluating the model. Amazon ML alerts you if some records in the evaluation datasource were invalid and were not included in the accuracy metric computation. For example, if the target variable is missing for some of the observations in the evaluation datasource, Amazon ML is unable to check if the ML model's predictions for these observations are correct. In this case, the records with missing target values are considered invalid.

  • Distribution of target variable

    Amazon ML shows you the distribution of the target attribute from the training and evaluation datasources so that you can review whether the target is distributed similarly in both datasources. If the model was trained on training data with a target distribution that differs from the distribution of the target on the evaluation data, then the quality of the evaluation could suffer because it is being computed on data with very different statistics. It is best to have the data distributed similarly over training and evaluation data, and have these datasets mimic as much as possible the data that the model will encounter when making predictions.

    If this alert triggers, try using the random split strategy to split the data into training and evaluation datasources. In rare cases, this alert might erroneously warn you about target distribution differences even though you split your data randomly. Amazon ML uses approximate data statistics to evaluate the data distributions, occasionally triggering this alert in error.