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…

1. Why do we tune?
2. Optimizing the models
3. Hyperparameter tuning
4. Steps for tuning
5. Data splits
6. Linear based models
7. How do you know when you know enough?
8. Basic rules of thumb
9. Buffer in time for spikes
10. Grid searching and automation

Sponsors

• Top End Devs
• Coaching | Top End Devs

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.