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Amazon Machine Learning
Developer Guide (Version Latest)

Data Insights

Amazon ML computes descriptive statistics on your input data that you can use to understand your data.

Descriptive Statistics

Amazon ML computes the following descriptive statistics for different attribute types:

Numeric:

  • Distribution histograms

  • Number of invalid values

  • Minimum, median, mean, and maximum values

Binary and categorical:

  • Count (of distinct values per category)

  • Value distribution histogram

  • Most frequent values

  • Unique values counts

  • Percentage of true value (binary only)

  • Most prominent words

  • Most frequent words

Text:

  • Name of the attribute

  • Correlation to target (if a target is set)

  • Total words

  • Unique words

  • Range of the number of words in a row

  • Range of word lengths

  • Most prominent words

Accessing Data Insights on the Amazon ML console

On the Amazon ML console, you can choose the name or ID of any datasource to view its Data Insights page. This page provides metrics and visualizations that enable you to learn about the input data associated with the datasource, including the following information:

  • Data summary

  • Target distributions

  • Missing values

  • Invalid values

  • Summary statistics of variables by data type

  • Distributions of variables by data type

The following sections describe the metrics and visualizations in greater detail.

Data Summary

The data summary report of a datasource displays summary information, including the datasource ID, name, where it was completed, current status, target attribute, input data information (S3 bucket location, data format, number of records processed and number of bad records encountered during processing) as well as the number of variables by data type.

Target Distributions

The target distributions report shows the distribution of the target attribute of the datasource. In the following example, there are 39,922 observations where the willRespondToCampaign target attribute equals 0. This is the number of customers who did not respond to the email campaign. There are 5,289 observations where willRespondToCampaign equals 1. This is the number of customers who responded to the email campaign.

Missing Values

The missing values report lists the attributes in the input data for which values are missing. Only attributes with numeric data types can have missing values. Because missing values can affect the quality of training an ML model, we recommend that missing values be provided, if possible.

During ML model training, if the target attribute is missing, Amazon ML rejects the corresponding record. If the target attribute is present in the record, but a value for another numeric attribute is missing, then Amazon ML overlooks the missing value. In this case, Amazon ML creates a substitute attribute and sets it to 1 to indicate that this attribute is missing. This allows Amazon ML to learn patterns from the occurrence of missing values.

Invalid Values

Invalid values can occur only with Numeric and Binary data types. You can find invalid values by viewing the summary statistics of variables in the data type reports. In the following examples, there is one invalid value in the duration Numeric attribute and two invalid values in the Binary data type (one in the housing attribute and one in the loan attribute).

Variable-Target Correlation

After you create a datasource, Amazon ML can evaluate the datasource and identify the correlation, or impact, between variables and the target. For example, the price of a product might have a significant impact on whether or not it is a best seller, while the dimensions of the product might have little predictive power.

It is generally a best practice to include as many variables in your training data as possible. However, the noise introduced by including many variables with little predictive power might negatively affect the quality and accuracy of your ML model.

You might be able to improve the predictive performance of your model by removing variables that have little impact when you train your model. You can define which variables are made available to the machine learning process in a recipe, which is a transformation mechanism of Amazon ML. To learn more about recipes, see Data Transformation for Machine Learning.

Summary Statistics of Attributes by Data Type

In the data insights report, you can view attribute summary statistics by the following data types:

  • Binary

  • Categorical

  • Numeric

  • Text

Summary statistics for the Binary data type show all binary attributes. The Correlations to target column shows the information shared between the target column and the attribute column. The Percent true column shows the percentage of observations that have value 1. The Invalid values column shows the number of invalid values as well as the percentage of invalid values for each attribute. The Preview column provides a link to a graphical distribution for each attribute.

Summary statistics for the Categorical data type show all Categorical attributes with the number of unique values, most frequent value, and least frequent value. The Preview column provides a link to a graphical distribution for each attribute.

Summary statistics for the Numeric data type show all Numeric attributes with the number of missing values, invalid values, range of values, mean, and median. The Preview column provides a link to a graphical distribution for each attribute.

Summary statistics for the Text data type show all of the Text attributes, the total number of words in that attribute, the number of unique words in that attribute, the range of words in an attribute, the range of word lengths, and the most prominent words. The Preview column provides a link to a graphical distribution for each attribute.

The next example shows the Text data type statistics for a text variable called review, with four records.

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1. The fox jumped over the fence. 2. This movie is intriguing. 3. 4. Fascinating movie.

The columns for this example would show the following information.

  • The Attributes column shows the name of the variable. In this example, this column would say "review."

  • The Correlations to target column exists only if a target is specified. Correlation measures the amount of information this attribute provides about the target. The higher the correlation, the more this attribute tells you about the target. Correlation is measured in terms of mutual information between a simplified representation of the text attribute and the target.

  • The Total words column shows the number of words generated from tokenizing each record, delimiting the words by white space. In this example, this column would say "12".

  • The Unique words column shows the number of unique words for an attribute. In this example, this column would say "10."

  • The Words in attribute (range) column shows the number of words in a single row in the attribute. In this example, this column would say "0-6."

  • The Word length (range) column shows the range of how many characters are in the words. In this example, this column would say "2-11."

  • The Most prominent words column shows a ranked list of words that appear in the attribute. If there is a target attribute, words are ranked by their correlation to the target, meaning that the words that have the highest correlation are listed first. If no target is present in the data, then the words are ranked by their entropy.

Understanding the Distribution of Categorical and Binary Attributes

By clicking the Preview link associated with a categorical or binary attribute, you can view that attribute's distribution as well as the sample data from the input file for each categorical value of the attribute.

For example, the following screenshot shows the distribution for the categorical attribute jobId. The distribution displays the top 10 categorical values, with all other values grouped as "other". It ranks each of the top 10 categorical values with the number of observations in the input file that contain that value, as well as a link to view sample observations from the input data file.

Understanding the Distribution of Numeric Attributes

To view the distribution of a numeric attribute, click the Preview link of the attribute. When viewing the distribution of a numeric attribute, you can choose bin sizes of 500, 200, 100, 50, or 20. The larger the bin size, the smaller number of bar graphs that will be displayed. In addition, the resolution of the distribution will be coarse for large bin sizes. In contrast, setting the bucket size to 20 increases the resolution of the displayed distribution.

The minimum, mean, and maximum values are also displayed, as shown in the following screenshot.

Understanding the Distribution of Text Attributes

To view the distribution of a text attribute, click the Preview link of the attribute. When viewing the distribution of a text attribute, you will see the following information.

Ranking

Text tokens are ranked by the amount of information they convey, most informative to least informative.

Token

Token shows the word from the input text that the row of statistics is about.

Word prominence

If there is a target attribute, words are ranked by their correlation to the target, so that words that have the highest correlation are listed first. If no target is present in the data, then the words are ranked by their entropy, ie, the amount of information they can communicate.

Count number

Count number shows the number of input records that the token appeared in.

Count percentage

Count percentage shows the percentage of input data rows the token appeared in.