Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines

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Presented by

Mark Fenner, Author of Machine Learning with Python for Everyone; Mark Pavletich, Domino Data Lab

About this talk

Models are at the heart of data science. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy using different techniques, methodologies, and settings, including hyperparameters. Regularized hyperparameters provide data scientists and researchers with the ability to control the flexibility of the model. This control prevents overfitting and reduction in predictive accuracy on new test data. When hyperparameters are also used for optimization and tuned for a specific dataset, they also impact predictive accuracy. In this hands-on code webinar, "Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines", data scientist and book author, Dr. Mark Fenner covers: - building a pipeline for automating ML workflow. - core concepts, including cross-validation. - how to compare, select, and tune hyperparameters. *this webinar is pre-recorded, but the content will be exclusively released to all registrants on December 4, 2019 at 10 am PT

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