EvalML: a library for automated machine learning and model understanding

R0:5f4f461a56b01fe699c2add0e2d0d703-EvalML: a library for automated machine learning and model understanding

🔘 Tutorial page: evalml.alteryx.com/en/stable/tutorials.html

Summary

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python. Combined with Featuretools and Compose, EvalML can be used to create end-to-end supervised machine learning solutions. “EvalML provides a simple, unified interface for building machine learning models, using those models to generate insights and to make accurate predictions. EvalML provides access to multiple modeling libraries under the same API. EvalML supports a variety of supervised machine learning problem types including regression, binary classification and multiclass classification. Custom objective functions let users phrase their search for a model directly in terms of what they value. Above all we’ve aimed to make EvalML stable and performant, with ML performance testing on every release” (Source: Dylan Sherry, EvalML Team Lead, Easy AutoML in Python, at: dnuggets.com/2021/04/easy-automl-python.html).


Author

Alteryx; Is a leader in Analytic Process Automation (APA). The Alteryx APA Platform™ unifies analytics, data science and business process automation in one easy-to-use platform to accelerate digital transformation.

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