An Introduction To Machine Learning Interpretability 1st Edition Patrick Hall And Navdeep Gill by Patrick Hall And Navdeep Gill 9781492033141, 1492033146 instant download after payment.

Understanding and trusting models and their results is a hallmark of good sci‐
ence. Scientists, engineers, physicians, researchers, and humans in general have
the need to understand and trust models and modeling results that affect their
work and their lives. However, the forces of innovation and competition are now
driving analysts and data scientists to try ever-more complex predictive modeling
and machine learning algorithms. Such algorithms for machine learning include
gradient-boosted ensembles (GBM), artificial neural networks (ANN), and ran‐
dom forests, among many others. Many machine learning algorithms have been
labeled “black box” models because of their inscrutable inner-workings. What
makes these models accurate is what makes their predictions difficult to under‐
stand: they are very complex. This is a fundamental trade-off. These algorithms
are typically more accurate for predicting nonlinear, faint, or rare phenomena.
Unfortunately, more accuracy almost always comes at the expense of interpreta‐
bility, and interpretability is crucial for business adoption, model documentation,
regulatory oversight, and human acceptance and trust.