Modify Count Table Parameters

Modifies the parameters used to create features from counts

Category: Learning with Counts


Applies to: Machine Learning Studio (classic)

This content pertains only to Studio (classic). Similar drag and drop modules have been added to Azure Machine Learning designer. Learn more in this article comparing the two versions.

Module overview

This article describes how to use the Modify Count Table Parameters module in Azure Machine Learning Studio (classic), to change the way that features are generated from a count table.

In general, to create count-based features, you use Build Counting Transform to process a dataset and create a count table, and from that count table generate a new set of features.

However, if you have already created a count table, you can use the Modify Count Table Parameters module to edit the definition of how the count data is processed. This lets you create a different set of count-based statistics based on the existing data, without having to re-analyze the dataset.

How to configure Modify Count Parameters

  1. Locate the transformation you want to modify, in the Transforms group, and add it to your experiment.

    You should have previously run an experiment that created a count transformation.

    • To modify a saved transform: Locate the transformation, in the Transforms group, and add it to your experiment.

    • To modify a count transformation created within the same experiment: If the transformation has not been saved, but is available as an output in the current experiment (for example, check the output of the Build Counting Transform module), you can use it directly by connecting the modules.

  2. Add the Modify Count Table Parameters module and connect the transformation as an input.

  3. In the Properties pane of the Modify Count Table Parameters module, type a value to use as theGarbage bin threshold.

    This value specifies the minimum number of occurrences that must be found for each feature value, in order for counts to be used. If the frequency of the value is less than the garbage bin threshold, the value-label pair is not counted as a discrete item; instead, all items with counts lower than the threshold value are placed in a single "garbage bin".

    If you are using a small dataset and you are counting and training on the same data, a good starting value is 1.

  4. For Additional prior pseudo examples, type a number that indicates the number of additional pseudo examples to include. You do not need to provide these examples; the pseudo examples are generated based on the prior distribution.

  5. For Laplacian noise scale, type a positive floating-point value that represents the scale used for introducing noise sampled from a Laplacian distribution. When you set a scale value, some acceptable level of noise is incorporated into the model, so the model is less likely to be affected by unseen values in data.

  6. In Output features include, choose the method to use when creating count-based features for inclusion in the transformation.

    • CountsOnly: Create features using counts.

    • LogOddsOnly: Create features using the log of the odds ratio.

    • BothCountsAndLogOdds: Create features using both counts and log odds.

  7. Select the Ignore back off column option if you want to override the IsBackOff flag in the output when creating features. When you select this option, count-based features are created even if the column doesn’t have significant count values.

  8. Run the experiment. You can then save the output of Modify Count Table Parameters as a new transformation, if desired.


For examples of how this module, see the Azure AI Gallery:

Technical notes

This section contains implementation details, tips, and answers to frequently asked questions.

It is statistically safe to count and train on the same data set if you set the Laplacian noise scale parameter.

Expected inputs

Name Type Description
Counting transform ITransform interface The counting transform to apply

Module parameters

Name Type Range Optional Default Description
Garbage bin threshold Float >=0.0f Required 10.0f The threshold under which a column value will be featurized against the garbage bin
Additional prior pseudo examples Float >=0.0f Required 42.0f The additional pseudo examples following prior distributions to be included
Laplacian noise scale Float >=0.0f Required 0.0f The scale of the Laplacian distribution from which noise is sampled
Output features include OutputFeatureType Required BothCountsAndLogOdds The features to output
Ignore back off column Boolean Required false Whether to ignore the IsBackOff column in the output


Name Type Description
Modified transform ITransform interface The modified transform


Exception Description
Error 0003 Exception occurs if one or more of inputs are null or empty.
Error 0086 Exception occurs when a counting transform is invalid.

For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.

For a list of API exceptions, see Machine Learning REST API Error Codes.

See also

Learning with Counts