Masís Interpretable Machine Learning with Python

Build explainable, fair, and robust high-performance models with hands-on, real-world examples
2. Auflage 2023
ISBN: 978-1-80324-362-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 606 Seiten

No detailed description available for "Interpretable Machine Learning with Python".

Autoren/Hrsg.

Masís, Serg

Weitere Mitwirkende

Molak, Aleksander

Rothman, Denis

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik

Inhaltsverzeichnis

Table of Contents - Interpretation, Interpretability and Explainability; and why does it all matter?
- Key Concepts of Interpretability
- Interpretation Challenges
- Global Model-agnostic Interpretation Methods
- Local Model-agnostic Interpretation Methods
- Anchors and Counterfactual Explanations
- Visualizing Convolutional Neural Networks
- Interpreting NLP Transformers
- Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
- Feature Selection and Engineering for Interpretability
- Bias Mitigation and Causal Inference Methods
- Monotonic Constraints and Model Tuning for Interpretability
- Adversarial Robustness
- What's Next for Machine Learning Interpretability?

Über Autor(innen)

Masís Serg:

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.Molak Aleksander:

Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.Rothman Denis:

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.