Author: Bogumił KamińskiPublisher:
ISBN: 9788380304246
Category :
Languages : en
Pages :
Book Description
Credit Scoring in Context of Interpretable Machine Learning
Author: Bogumił KamińskiPublisher:
ISBN: 9788380304246
Category :
Languages : en
Pages :
Book Description
INTERPRETABLE MACHINE LEARNING CREDIT SCORING MODEL
Author: FOO YEONG JIN (TP044538)Publisher:
ISBN:
Category : Credit scoring systems
Languages : en
Pages : 55
Book Description
This study divided into two distinct part to accurately assess the interpretability of the proposed model. The first part is to develop a credit scoring model using the black-box algorithm, and the second part is to develop an interpretable model to interpret the black-box model. The extreme gradient boosting (xgboost) is used as the black-box algorithm and Local Interpretable Model-agnostic Explanations (LIME) is used as interpretation model. For the evaluation, this study is using two different assessment to measure the interpretability of the interpretation model. The first assessment is to measure the consistency through the weight assigned to each independent variable in both training and validation data. The second assessment is the interpretability, which signage assigned to each independent variable is used to compare against the finding from the literature. The result shows that the proposed model is achieved the desire consistency and interpretability. The difference between training and validation data is less than 5%. The interpretability is also same as the finding gathered from the literature.
Interpretable Machine Learning
Author: Christoph MolnarPublisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320
Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Rule Extraction from Support Vector Machines
Author: Joachim DiederichPublisher: Springer
ISBN: 3540753907
Category : Technology & Engineering
Languages : en
Pages : 267
Book Description
Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
Recent Methods from Statistics and Machine Learning for Credit Scoring
Author: Anne KrausPublisher: Cuvillier Verlag
ISBN: 3736947364
Category : Mathematics
Languages : en
Pages : 166
Book Description
Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.
Interpretable Machine Learning with Python
Author: Serg MasísPublisher: Packt Publishing Ltd
ISBN: 1800206577
Category : Computers
Languages : en
Pages : 737
Book Description
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.
Machine-Learning Credit Scores and Disparate Impact Theory
Author: Lauri KaiPublisher:
ISBN:
Category :
Languages : en
Pages : 36
Book Description
This Note analyzes the effects of machine learning in the lending context and argues that the existing legal framework can address unintentional discrimination that may result from credit-scoring models developed through machine learning. Potential liability stems from increased complexity of machine-learning processes; as machine-learning algorithms become more sophisticated, it becomes more difficult to explain the results they produce. Under current law, the inability to reasonably explain or even discover the correlations between data inputs and the resulting disparate impact leaves the lender vulnerable to suit for unintentional discrimination.
Credit Scoring and Its Applications, Second Edition
Author: Lyn ThomasPublisher: SIAM
ISBN: 1611974550
Category : Business & Economics
Languages : en
Pages : 380
Book Description
Credit Scoring and Its Applications?is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives.? New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.?
Explainable and Interpretable Models in Computer Vision and Machine Learning
Author: Hugo Jair EscalantePublisher: Springer
ISBN: 3319981315
Category : Computers
Languages : en
Pages : 305
Book Description
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Credit Risk Scorecards
Author: Naeem SiddiqiPublisher: John Wiley & Sons
ISBN: 1118429168
Category : Business & Economics
Languages : en
Pages : 124
Book Description
Praise for Credit Risk Scorecards "Scorecard development is important to retail financial services in terms of credit risk management, Basel II compliance, and marketing of credit products. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation. The book should be compulsory reading for modern credit risk managers." —Michael C. S. Wong Associate Professor of Finance, City University of Hong Kong Hong Kong Regional Director, Global Association of Risk Professionals "Siddiqi offers a practical, step-by-step guide for developing and implementing successful credit scorecards. He relays the key steps in an ordered and simple-to-follow fashion. A 'must read' for anyone managing the development of a scorecard." —Jonathan G. Baum Chief Risk Officer, GE Consumer Finance, Europe "A comprehensive guide, not only for scorecard specialists but for all consumer credit professionals. The book provides the A-to-Z of scorecard development, implementation, and monitoring processes. This is an important read for all consumer-lending practitioners." —Satinder Ahluwalia Vice President and Head-Retail Credit, Mashreqbank, UAE "This practical text provides a strong foundation in the technical issues involved in building credit scoring models. This book will become required reading for all those working in this area." —J. Michael Hardin, PhD Professor of StatisticsDepartment of Information Systems, Statistics, and Management ScienceDirector, Institute of Business Intelligence "Mr. Siddiqi has captured the true essence of the credit risk practitioner's primary tool, the predictive scorecard. He has combined both art and science in demonstrating the critical advantages that scorecards achieve when employed in marketing, acquisition, account management, and recoveries. This text should be part of every risk manager's library." —Stephen D. Morris Director, Credit Risk, ING Bank of Canada
