Hyperight

Automation in SAS Visual Data Mining and Machine Learning – Wendy Czika & Josefin Rosén, SAS Institute

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In this session you can learn how automated machine learning can help every data scientist, from the novice to the most experienced practitioner enabling you to focus on solving the problem at hand:

1) You can choose to have features automatically constructed or to automate the process of algorithm selection and hyperparameter tuning by using dedicated Model Studio nodes in the pipeline that represents your machine learning process.

2) You can build on or edit a pipeline that includes these nodes, inserting your domain expertise into the process.

3) You can ask the software to automatically build an entire pipeline that includes various feature engineering steps and predictive models, optimized for your specific data according to the assessment criterion of your choice.

The included models are determined using hyperparameter tuning across multiple modeling algorithms. Not only do these automation techniques aid and accelerate the modeling process for beginning users, but they also relieve expert data scientists of the burden of iterating through various feature engineering steps, model hyperparameter values, and modeling algorithms, enabling them to focus on solving the problem at hand.

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