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Author: Timothy Masters Publisher: Apress ISBN: 9781484233351 Category : Computers Languages : en Pages : 517
Book Description
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn Compute entropy to detect problematic predictors Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising Use Monte-Carlo permutation methods to assess the role of good luck in performance results Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Author: Timothy Masters Publisher: Apress ISBN: 9781484233351 Category : Computers Languages : en Pages : 517
Book Description
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn Compute entropy to detect problematic predictors Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising Use Monte-Carlo permutation methods to assess the role of good luck in performance results Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Author: Timothy Masters Publisher: CreateSpace ISBN: 9781484137451 Category : Mathematics Languages : en Pages : 562
Book Description
This book begins by presenting methods for performing practical, real-life assessment of the performance of prediction and classification models. It then goes on to discuss techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment. Finally, a hundred pages are devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique. The ultimate purpose of this text is three-fold. The first goal is to open the eyes of serious developers to some of the hidden pitfalls that lurk in the model development process. The second is to provide broad exposure for some of the most powerful model enhancement algorithms that have emerged from academia in the last two decades, while not bogging down readers in cryptic mathematical theory. Finally, this text should provide the reader with a toolbox of ready-to-use C++ code that can be easily incorporated into his or her existing programs.
Author: Timothy Masters Publisher: Apress ISBN: 1484233360 Category : Computers Languages : en Pages : 530
Book Description
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn Compute entropy to detect problematic predictors Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising Use Monte-Carlo permutation methods to assess the role of good luck in performance results Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Author: Timothy Masters Publisher: Apress ISBN: 1484241738 Category : Computers Languages : en Pages : 325
Book Description
Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not live up to expectations. This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book. What You Will Learn See how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.
Author: Hasan Yazici Publisher: Springer ISBN: 3319083740 Category : Medical Languages : en Pages : 285
Book Description
It is imperative that health professionals caring for patients with rheumatic diseases understand how to correctly interpret evidence in their field, taking into account the merits and shortcomings of available data. Understanding Evidence-Based Rheumatology offers a practical assessment of criteria, drugs, trials, and registries and provides useful tools for successfully interpreting this data. The book introduces readers to basic analysis of trial design, statistics and application of data through no-nonsense, easy-to-follow insights. Using numerous examples, chapters outline the difficulties physicians encounter when measuring disease activity in rheumatology and offer strategies for systematically approaching these situations. Ethical issues in study design and reporting are examined and the book closes with a summary of future directions for scientific and clinical studies in rheumatology. Understanding Evidence-Based Rheumatology is an invaluable resource for trainees, clinicians and scientists, preparing them with the necessary tools to correctly gather evidence and shed light on the difficult practice of rheumatology.
Author: Hau-wen Chang Publisher: ISBN: Category : Languages : en Pages :
Book Description
In the dominance of social networks era, vast information is created and shared across the world each day. The uniqueness and the prevalence of these user-generated content present both challenges and opportunities. In this thesis, in particular, we study several tasks on mining the user-generated content with regard to textual content and link-based content.First, we study the home location estimation for Twitter users from their shared textual content. We employ Gaussian Mixture Model to compensate the drawback in the Maximum Likelihood Estimation. We propose two unsupervised feature selection methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data in the content. Second, we study the item recommendation problem with a broader view of a social network system. By taking various relationships into consideration, the data sparseness problem common in recommendation tasks are alleviated. Based on the same characteristics principle, we propose a matrix co-factorization framework with a shared latent space to optimize the recommendation collectively. Several algorithms are proposed under the framework to exploit intricate relationships in a social network system. Finally, we investigate the effectiveness of classification with the imperfect textual content extracted from videos, where often very limited information is available. Through means of automatic recognition techniques, some link-based content is enriched with a trade-off of incorrectness. We also propose a heuristics-based method to extract n-gram keyphrases from noisy textual content by taking the importance of sub-term keywords into consideration.
Author: Juan A. Asensio Publisher: Elsevier Health Sciences ISBN: 0323070868 Category : Medical Languages : en Pages : 817
Book Description
Here’s a unified evidence-based approach to problems encountered in trauma and critical care surgical situations. Comprehensive and concise, it is ideal for a quick overview before entering the operating room or ICU, or as a review for board certification or recertification. Be prepared for the unexpected with practical, concise coverage of major surgical problems in trauma and critical care. Get expert practical and up-to-date guidance on ventilator management, damage control, noninvasive techniques, imaging, infection control, dealing with mass casualties, treating injuries induced by chemical and biological agents, and much more. Find the information you need quickly and easily through numerous illustrations, key points boxes, algorithms, and tables.
Author: Faye Taxman Publisher: Taylor & Francis ISBN: 1317402839 Category : Social Science Languages : en Pages : 493
Book Description
The Handbook on Risk and Need Assessment: Theory and Practice covers risk assessments for individuals being considered for parole or probation. Evidence-based approaches to such decisions help take the emotion and politics out of community corrections. As the United States begins to back away from ineffective, expensive policies of mass incarceration, this handbook will provide the resources needed to help ensure both public safety and the effective rehabilitation of offenders. The ASC Division on Corrections & Sentencing Handbook Series will publish volumes on topics ranging from violence risk assessment to specialty courts for drug users, veterans, or the mentally ill. Each thematic volume focuses on a single topical issue that intersects with corrections and sentencing research.
Author: Shelley L. Brown Publisher: John Wiley & Sons ISBN: 1119886414 Category : Psychology Languages : en Pages : 468
Book Description
The Wiley Handbook on What Works with Girls and Women in Conflict with the Law The most practical discussion of the rehabilitation of girls and women in conflict with the law in the correctional arena What Works with Girls and Women in Conflict with the Law is the leading examination of evidence-based practice in the field of gender-responsive corrections. Adopting an international and intersectional approach, the distinguished authors seek to collect the best available data and thinking on what works with girls and women and apply it to the real-world problems facing correctional systems today. As part of its contextual and rich approach to the subject, What Works with girls and women in conflict with the law, covers a broad variety of topics, ranging from theories of female involvement in crime, security classification and risk assessment, evidence-based treatment and supervision approaches, special populations (such as Indigenous women), to legal/policy developments in the field of gender-responsive corrections. Perfect for students and practitioners in the field of psychology, criminology, social work, criminal justice, and corrections, this is the only reference of its kind to focus on the practical applications of the latest theory.
Author: Christoph Molnar Publisher: 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.
Author: Timothy Masters
Author: Timothy Masters
Author: Timothy Masters
Author: Timothy Masters
Author: Timothy Masters
Author: Hasan Yazici
Author: Hau-wen Chang
Author: Juan A. Asensio
Author: Faye Taxman
Author: Shelley L. Brown
Author: Christoph Molnar