| | Why Evaluation of Machine Learning Is Harder Than You Think and What to Do About That | | | | | | Hi David,
Evaluation of machine learning models and the impact of these learning projects on serious business goals is difficult, but without valid evaluation, how can data scientists build and improve effective learning systems? And how can business leaders, product managers, architects, data engineers and executives who need to make decisions about machine learning do that well if they have no understanding of how to evaluate the success of a machine learning system?
There are fundamental approaches that can improve the quality of machine learning evaluation, and they are important for data scientists and non-data scientists to understand. They also aren’t what you learned in a machine learning class or Kaggle contest. Join Ellen Friedman, Principal Technologist at MapR, on April 23rd for a webinar on how to do a better evaluation of machine learning models and projects.
Without diving into scary math and all the technical details, Ellen will explain on a conceptual but still sound level why evaluation is challenging and will describe some key approaches to address these problems. These approaches will be grounded in real-world examples across different industries.
Regards,
MapR Team | | | | | |
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