000			02047cam a22002297i 4500
008			230703t20222023cc a b 001 0 eng d
015			_aGBC2J5983 _2bnb
016	7		_a020791265 _2Uk
020			_a9789355422439
042			_alccopycat
082	0	4	_a005.74015 _bMUN
100	1		_aMunn, Michael
245	1	0	_aExplainable AI for practitioners : _bdesigning and implementing explainable ML solutions
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2023
300			_axvi, 259 pages :
520			_aMost 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 techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
650		0	_aMachine learning.
650		7	_aMachine learning. _2fast
700	1		_aPitman, David _c(Engineer),
906			_aBusiness Analytics
942			_2ddc _c1
999			_c98116 _d98116