Past Recording
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Model Selection for Optimal Prediction in Statistical Learning - Part 2 / 2
Wednesday May 27 2020 16:00 GMT
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Model Selection for Optimal Prediction in Statistical Learning - Part 2 / 2
Why This Is Interesting

Professor Ernest will walk us through a statistical framework for model selection. His emphasis on investigating underlying probabilistic phenomenon is crucial to a methodical understanding of how the data behaves. This consistency will be shown through every step of the modelling journey; choosing the most appropriate metric for model accuracy and likelihood function, aggregation techniques, and how to evaluate model performance from a probabilistic and statistical perspective.

Discussion Points
  • Choosing appropriate metrics for model accuracy
  • Choosing appropriate likelihood functions
  • Aggregation techniques
  • Model performance evaluation … But from a probabilistic perspective
Takeaways
  • Professor Ernest walked us through how to choose our function space by teaching us how to go about choosing our estimator functions.
  • He then showed us how to make decisions regarding refining our function space by showing us how to cross-validate and aggregate our models.
  • Finally, he walked us through how to determine the success of our function space by demonstrating how tools like confidence intervals and VC dimensions can be leveraged to measure accuracy.
Time of Recording: Wednesday May 27 2020 16:00 GMT