Merge Count Transform

You can use the Merge Count Transform module to combine two sets of count-based features. By merging two sets of related counts and features, you can potentially improve the coverage and distribution of the features.

In general, learning from counts is particularly useful in large data sets with high-cardinality features. Therefore, the ability to combine multiple datasets into count-based feature sets without having to reprocess the datasets makes it easier to gather statistics on very large datasets and apply them to new datasets. For example, count tables can be used to collect information over terabytes of data. You can re-use those statistics to improve the accuracy of predictive models on small data sets.

To merge two sets of count-based features, the features must have been created using tables that have the same schema: that is, both sets must use the same columns, with the same names and data types.

You can see examples of how this module is used by exploring these sample experiments in the Model Gallery:

• The Learning with Counts: Binary Classification sample demonstrates how to use the learning with counts modules to generate features from columns of categorical values for a binary classification model.

• The Learning with Counts: Multiclass classification with NYC taxi data sample demonstrates how to use the learning with counts modules for performing multiclass classification on the publicly available NYC taxi dataset. The sample uses a multiclass logistic regression learner to model this problem.

• The Learning with Counts: Binary classification with NYC taxi data sample demonstrates how to use the learning with counts modules for performing binary classification on the publicly available NYC taxi dataset. The sample uses a two-class logistic regression learner to model this problem.