US 2022/0101190 A1Published application

System and method of operationalizing automated feature engineering

AssigneeAlteryx, Inc.

InventorsJames Max Kanter, Kalyan Kumar Veeramachaneni

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Abstract

A feature engineering tool automatically generates a group of features for training a machine learning model. The feature engineering tool selects primitives for a dataset and synthesizes a plurality of features based on the selective primitives and the dataset. The feature engineering tool iteratively applies the plurality of features to a different portion of the dataset to evaluate usefulness of the plurality of features. Based on the evaluation, it removes some of the plurality of features to obtain the group of features. The feature engineering tool also determines the importance factor for each feature in the group. The machine learning model is generated based on the features and their importance factors and can be used to make a prediction based on new data.

Claims20

Dependent claims

A computer implemented method, comprising:

receiving a dataset from a data source;
selecting primitives from a pool of primitives based on the received dataset, each of the selected primitives configured to be applied to at least a portion of the dataset to synthesize one or more features;
synthesizing a plurality of features by applying the selected primitives to the received dataset;
iteratively evaluating the plurality of features to remove some features from the plurality of features to obtain a subset of features, each iteration comprising:
evaluating usefulness of at least some features of the plurality of features by applying a different portion of the dataset to the evaluated features, and
removing some of the evaluated features based on the usefulness of the evaluated features to produce the subset of features;
determining an importance factor for each feature of the subset of features;
and
generating a machine learning model based on the subset of features and the importance factor of each feature of the subset of features, the machine learning model configured to be used to make a prediction based on new data.

6 more elements

A system, comprising:

a computer processor for executing computer program instructions;
and
a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations comprising:
receiving a dataset from a data source;
selecting primitives from a pool of primitives based on the received dataset, each of the selected primitives configured to be applied to at least a portion of the dataset to synthesize one or more features;
synthesizing a plurality of features by applying the selected primitives to the received dataset;
iteratively evaluating the plurality of features to remove some features from the plurality of features to obtain a subset of features, each iteration comprising:
evaluating usefulness of at least some features of the plurality of features by applying a different portion of the dataset to the evaluated features, and
removing some of the evaluated features based on the usefulness of the evaluated features to produce the subset of features;
determining an importance factor for each feature of the subset of features;
and
generating a machine learning model based on the subset of features and the importance factor of each feature of the subset of features, the machine learning model configured to be used to make a prediction based on new data.

9 more elements

A non-transitory computer-readable memory storing executable computer program instructions for processing data blocks in a data analytics system, the instructions executable to perform operations comprising:

receiving a dataset from a data source;
selecting primitives from a pool of primitives based on the received dataset, each of the selected primitives configured to be applied to at least a portion of the dataset to synthesize one or more features;
synthesizing a plurality of features by applying the selected primitives to the received dataset;
iteratively evaluating the plurality of features to remove some features from the plurality of features to obtain a subset of features, each iteration comprising:
evaluating usefulness of at least some features of the plurality of features by applying a different portion of the dataset to the evaluated features, and
removing some of the evaluated features based on the usefulness of the evaluated features to produce the subset of features;
determining an importance factor for each feature of the subset of features;
and
generating a machine learning model based on the subset of features and the importance factor of each feature of the subset of features, the machine learning model configured to be used to make a prediction based on new data.

6 more elements

Dependencies are inferred from the claim text.