Hyperparameter Tuning for Machine Learning Models - ML 079

When developing ML models, defining and selecting the model architecture will be fundamental to ensure the best possible outcomes.  Parameters that define the model architecture are referred to as hyperparameters and the process of searching for the ideal model architecture is referred to as hyperparameter tuning.  Today on the show, Ben and Michael discuss hyperparameter tuning and how to implement this into your ML modeling.

In this episode…

Why do we tune?
Optimizing the models
Hyperparameter tuning
Steps for tuning
Data splits
Linear based models
How do you know when you know enough?
Basic rules of thumb
Buffer in time for spikes
Grid searching and automation

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