Splitting Your Data - Amazon Machine Learning
Pre-splitting Your DataSequentially Splitting Your DataRandomly Splitting Your Data

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Splitting Your Data

The fundamental goal of an ML model is to make accurate predictions on future data instances beyond those used to train models. Before using an ML model to make predictions, we need to evaluate the predictive performance of the model. To estimate the quality of an ML models predictions with data it has not seen, we can reserve, or split, a portion of the data for which we already know the answer as a proxy for future data and evaluate how well the ML model predicts the correct answers for that data. You split the datasource into a portion for the training datasource and a portion for the evaluation datasource.

Amazon ML provides three options for splitting your data:

  • Pre-split the data - You can split the data into two data input locations, before uploading them to Amazon Simple Storage Service (Amazon S3) and creating two separate datasources with them.

  • Amazon ML sequential split - You can tell Amazon ML to split your data sequentially when creating the training and evaluation datasources.

  • Amazon ML random split - You can tell Amazon ML to split your data using a seeded random method when creating the training and evaluation datasources.

Pre-splitting Your Data

If you want explicit control over the data in your training and evaluation datasources, split your data into separate data locations, and create a separate datasources for the input and evaluation locations.

Sequentially Splitting Your Data

A simple way to split your input data for training and evaluation is to select non-overlapping subsets of your data while preserving the order of the data records. This approach is useful if you want to evaluate your ML models on data for a certain date or within a certain time range. For example, say that you have customer engagement data for the past five months, and you want to use this historical data to predict customer engagement in the next month. Using the beginning of the range for training, and the data from the end of the range for evaluation might produce a more accurate estimate of the model’s quality than using records data drawn from the entire data range.

The following figure shows examples of when you should use a sequential splitting strategy versus when you should use a random strategy.

Comparison of sequential and random splitting strategies for time series data.

When you create a datasource, you can choose to split your datasource sequentially, and Amazon ML uses the first 70 percent of your data for training and the remaining 30 percent of the data for evaluation. This is the default approach when you use the Amazon ML console to split your data.

Randomly Splitting Your Data

Randomly splitting the input data into training and evaluation datasources ensures that the distribution of the data is similar in the training and evaluation datasources. Choose this option when you don't need to preserve the order of your input data.

Amazon ML uses a seeded pseudo-random number generation method to split your data. The seed is based partly on an input string value and partially on the content of the data itself. By default, the Amazon ML console uses the S3 location of the input data as the string. API users can provide a custom string. This means that given the same S3 bucket and data, Amazon ML splits the data the same way every time. To change how Amazon ML splits the data, you can use the CreateDatasourceFromS3, CreateDatasourceFromRedshift, or CreateDatasourceFromRDS API and provide a value for the seed string. When using these APIs to create separate datasources for training and evaluation, it is important to use the same seed string value for both datasources and the complement flag for one datasource, to ensure that there is no overlap between the training and evaluation data.

Diagram showing data split into training and evaluation sets using a seed-based method.

A common pitfall in developing a high-quality ML model is evaluating the ML model on data that is not similar to the data used for training. For example, say you are using ML to predict the genre of movies, and your training data contains movies from the Adventure, Comedy, and Documentary genres. However, your evaluation data contains only data from the Romance and Thriller genres. In this case, the ML model did not learn any information about the Romance and Thriller genres, and the evaluation did not evaluate how well the model learned patterns for the Adventure, Comedy, and Documentary genres. As a result, the genre information is useless, and the quality of the ML model predictions for all of the genres is compromised. The model and evaluation are too dissimilar (have extremely different descriptive statistics) to be useful. This can happen when input data is sorted by one of the columns in the dataset, and then split sequentially.

If your training and evaluation datasources have different data distributions, you will see an evaluation alert in your model evaluation. For more information about evaluation alerts, see Evaluation Alerts.

You do not need to use random splitting in Amazon ML if you have already randomized your input data, for example, by randomly shuffling your input data in Amazon S3, or by using a Amazon Redshift SQL query's random() function or a MySQL SQL query's rand() function when creating the datasources. In these cases, you can rely on the sequential split option to create training and evaluation datasources with similar distributions.