Creating a Data Schema for Amazon ML - 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.

Creating a Data Schema for Amazon ML

A schema is composed of all attributes in the input data and their corresponding data types. It allows Amazon ML to understand the data in the datasource. Amazon ML uses the information in the schema to read and interpret the input data, compute statistics, apply the correct attribute transformations, and fine-tune its learning algorithms. If you don't provide a schema, Amazon ML infers one from the data.

Example Schema

For Amazon ML to read the input data correctly and produce accurate predictions, each attribute must be assigned the correct data type. Let's walk through an example to see how data types are assigned to attributes, and how the attributes and data types are included in a schema. We'll call our example "Customer Campaign" because we want to predict which customers will respond to our email campaign. Our input file is a .csv file with nine columns:

1,3,web developer,basic.4y,no,no,1,261,0 2,1,car repair,,no,no,22,149,0 3,1,car mechanic,,yes,no,65,226,1 4,2,software developer,basic.6y,no,no,1,151,0

This the schema for this data:

{     "version": "1.0",     "rowId": "customerId",     "targetAttributeName": "willRespondToCampaign",     "dataFormat": "CSV",     "dataFileContainsHeader": false,     "attributes": [         {             "attributeName": "customerId",             "attributeType": "CATEGORICAL"         },         {             "attributeName": "jobId",             "attributeType": "CATEGORICAL"         }, { "attributeName": "jobDescription", "attributeType": "TEXT" },         {             "attributeName": "education",             "attributeType": "CATEGORICAL"         },         {             "attributeName": "housing",             "attributeType": "CATEGORICAL"         },         {             "attributeName": "loan",             "attributeType": "CATEGORICAL"         },         {             "attributeName": "campaign",             "attributeType": "NUMERIC"         },         {             "attributeName": "duration",             "attributeType": "NUMERIC"         }, { "attributeName": "willRespondToCampaign", "attributeType": "BINARY" } ] }

In the schema file for this example, the value for the rowId is customerId:

"rowId": "customerId",

The attribute willRespondToCampaign is defined as the target attribute:

"targetAttributeName": "willRespondToCampaign ",

The customerId attribute and the CATEGORICAL data type are associated with the first column, the jobId attribute and the CATEGORICAL data type are associated with the second column, the jobDescription attribute and the TEXT data type are associated with the third column, the education attribute and the CATEGORICAL data type are associated with the fourth column, and so on. The ninth column is associated with the willRespondToCampaign attribute with a BINARY data type, and this attribute also is defined as the target attribute.

Using the targetAttributeName Field

The targetAttributeName value is the name of the attribute that you want to predict. You must assign a targetAttributeName when creating or evaluating a model.

When you are training or evaluating an ML model, the targetAttributeName identifies the name of the attribute in the input data that contains the "correct" answers for the target attribute. Amazon ML uses the target, which includes the correct answers, to discover patterns and generate a ML model.

When you are evaluating your model, Amazon ML uses the target to check the accuracy of your predictions. After you have created and evaluated the ML model, you can use data with an unassigned targetAttributeName to generate predictions with your ML model.

You define the target attribute in the Amazon ML console when you create a datasource, or in a schema file. If you create your own schema file, use the following syntax to define the target attribute:

"targetAttributeName": "exampleAttributeTarget",

In this example, exampleAttributeTarget is the name of the attribute in your input file that is the target attribute.

Using the rowID Field

The row ID is an optional flag associated with an attribute in the input data. If specified, the attribute marked as the row ID is included in the prediction output. This attribute makes it easier to associate which prediction corresponds with which observation. An example of a good row ID is a customer ID or a similar unique attribute.


The row ID is for your reference only. Amazon ML doesn't use it when training an ML model. Selecting an attribute as a row ID excludes it from being used for training an ML model.

You define the row ID in the Amazon ML console when you create a datasource, or in a schema file. If you are creating your own schema file, use the following syntax to define the row ID:

"rowId": "exampleRow",

In the preceding example, exampleRow is the name of the attribute in your input file that is defined as the row ID.

When generating batch predictions, you might get the following output:

tag,bestAnswer,score 55,0,0.46317 102,1,0.89625

In this example, RowID represents the attribute customerId. For example, customerId 55 is predicted to respond to our email campaign with low confidence (0.46317) , while customerId 102 is predicted to respond to our email campaign with high confidence (0.89625).

Using the AttributeType Field

In Amazon ML, there are four data types for attributes:


Choose BINARY for an attribute that has only two possible states, such as yes or no.

For example, the attribute isNew, for tracking whether a person is a new customer, would have a true value to indicate that the individual is a new customer, and a false value to indicate that he or she is not a new customer.

Valid negative values are 0, n, no, f, and false.

Valid positive values are 1, y, yes, t, and true.

Amazon ML ignores the case of binary inputs and strips the surrounding white space. For example, " FaLSe " is a valid binary value. You can mix the binary values that you use in the same datasource, such as using true, no, and 1. Amazon ML outputs only 0 and 1 for binary attributes.


Choose CATEGORICAL for an attribute that takes on a limited number of unique string values. For example, a user ID, the month, and a zip code are categorical values. Categorical attributes are treated as a single string, and are not tokenized further.


Choose NUMERIC for an attribute that takes a quantity as a value.

For example, temperature, weight, and click rate are numeric values.

Not all attributes that hold numbers are numeric. Categorical attributes, such as days of the month and IDs, are often represented as numbers. To be considered numeric, a number must be comparable to another number. For example, the customer ID 664727 tells you nothing about the customer ID 124552, but a weight of 10 tells you that that attribute is heavier than an attribute with a weight of 5. Days of the month are not numeric, because the first of one month could occur before or after the second of another month.


When you use Amazon ML to create your schema, it assigns the Numeric data type to all attributes that use numbers. If Amazon ML creates your schema, check for incorrect assignments and set those attributes to CATEGORICAL.


Choose TEXT for an attribute that is a string of words. When reading in text attributes, Amazon ML converts them into tokens, delimited by white spaces.

For example, email subject becomes email and subject, and email-subject here becomes email-subject and here.

If the data type for a variable in the training schema does not match the data type for that variable in the evaluation schema, Amazon ML changes the evaluation data type to match the training data type. For example, if the training data schema assigns a data type of TEXT to the variable age, but the evaluation schema assigns a data type of NUMERIC to age, then Amazon ML treats the ages in the evaluation data as TEXT variables instead of NUMERIC.

For information about statistics associated with each data type, see Descriptive Statistics.

Providing a Schema to Amazon ML

Every datasource needs a schema. You can choose from two ways to provide Amazon ML with a schema:

  • Allow Amazon ML to infer the data types of each attribute in the input data file and automatically create a schema for you.

  • Provide a schema file when you upload your Amazon Simple Storage Service (Amazon S3) data.

Allowing Amazon ML to Create Your Schema

When you use the Amazon ML console to create a datasource, Amazon ML uses simple rules, based on the values of your variables, to create your schema. We strongly recommend that you review the Amazon ML-created schema, and correct the data types if they aren't accurate.

Providing a Schema

After you create your schema file, you need to make it available to Amazon ML. You have two options:

  1. Provide the schema by using the Amazon ML console.

    Use the console to create your datasource, and include the schema file by appending the .schema extension to the file name of your input data file. For example, if the Amazon Simple Storage Service (Amazon S3) URI to your input data is s3://my-bucket-name/data/input.csv, the URI to your schema will be s3://my-bucket-name/data/input.csv.schema. Amazon ML automatically locates the schema file that you provide instead of attempting to infer the schema from your data.

    To use a directory of files as your data input to Amazon ML, append the .schema extension to your directory path. For example, if your data files reside in the location s3://examplebucket/path/to/data/, the URI to your schema will be s3://examplebucket/path/to/data/.schema.

  2. Provide the schema by using the Amazon ML API.

    If you plan to call the Amazon ML API to create your datasource, you can upload the schema file into Amazon S3, and then provide the URI to that file in the DataSchemaLocationS3 attribute of the CreateDataSourceFromS3 API. For more information, see CreateDataSourceFromS3.

    You can provide the schema directly in the payload of CreateDataSource* APIs instead of first saving it to Amazon S3. You do this by placing the full schema string in the DataSchema attribute of CreateDataSourceFromS3, CreateDataSourceFromRDS, or CreateDataSourceFromRedshift APIs. For more information, see the Amazon Machine Learning API Reference.