Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the...
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the...
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.
Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.
This essential book provides:
A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these
Chybí či je nepravdivý některý důležitý parametr? Uvedené informace jsou pouze orientační, před zakoupením ve vybraném obchodě doporučujeme ověřit, že prodávaný model má klíčové vlastnosti dle vašich požadavků. I když se snažíme o maximální přesnost informací, bohužel nemůžeme zaručit jeho 100% správnost. Ceny produktů jsou uváděny včetně DPH.
