Alteryx, Inc.

James Max Kanter

Kalyan Kumar Veeramachaneni

A machine learning application is selected from a plurality of machine learning applications. Each machine learning application corresponds to a different industry problem and includes standard features and machine learning pipelines specific to the corresponding industrial problem. The machine learning application receives a dataset for generating a model for making a prediction for the industrial problem corresponding to the selected machine learning application. The standard features are provided for display for the user to map variables in the dataset to the standard features. Mapping by the user is received through the user interface. The machine learning pipelines are applied to the dataset to train a plurality of models based at least on the mapping. The trained models are ranked and one of the trained models is selected based on the ranking. The selected trained model is to be used for making the prediction based on new data.

Industry specific machine learning applications

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.

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Boston, MA, United States of America

James Max Kanter