Model Selection for Optimal Prediction in Statistical Learning - Part 2 / 2

Time: Wednesday 27-May-2020 16:00 (This is a past event.)

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    Motivation / Abstract
    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.
    Questions Discussed
    - Choosing appropriate metrics for model accuracy
    - Choosing appropriate likelihood functions
    - Aggregation techniques
    - Model performance evaluation
    ... But from a probabilistic perspective
    Key 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.  
    Stream Categories:
     SpotlightAuthor SpeakingML Interpretability