Description: Supervised Machine Learning by Tanya Kolosova, Samuel Berestizhevsky AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods. FORMAT Paperback CONDITION Brand New Publisher Description AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesnt ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub Author Biography Tanya Kolosova is a statistician, software engineer, an educator, and a co-author of two books on statistical analysis and metadata-based applications development using SAS. Tanya is an actionable analytics expert, she has extensive knowledge of software development methods and technologies, artificial intelligence methods and algorithms, and statistically designed experiments. Samuel Berestizhevsky is a statistician, researcher and software engineer. Together with Tanya, Samuel co-authored two books on statistical analysis and metadata-based applications development using SAS. Samuel is an innovator and an expert in the area of automated actionable analytics and artificial intelligence solutions. His extensive knowledge of software development methods, technologies and algorithms allows him to develop solutions on the cutting edge of science. Table of Contents Introduction. PART 1 1.Introduction to the AI framework. 2.Supervised Machine Learning and Its Deployment in SAS and R. 3.Bootstrap methods and Its Deployment in SAS and R. 4.Outliers Detection and Its Deployment in SAS and R. 5.Design of Experiment and Its Deployment in SAS and R. PART II 1.Introduction to the SAS and R based table-driven environment. 2.Input Data component. 3.Design of Experiment for Machine-Learning component. 4."Contaminated" Training Datasets Component. PART III 1.Insurance Industry: Underwriters decision-making process. 2.Insurance Industry: Claims Modeling and Prediction. Index. Details ISBN0367538822 Author Samuel Berestizhevsky Pages 182 Publisher Taylor & Francis Ltd Year 2022 ISBN-10 0367538822 ISBN-13 9780367538828 Publication Date 2022-04-29 UK Release Date 2022-04-29 Format Paperback Place of Publication London Country of Publication United Kingdom AU Release Date 2022-04-29 NZ Release Date 2022-04-29 Illustrations 59 Tables, black and white; 22 Line drawings, black and white; 22 Illustrations, black and white Subtitle Optimization Framework and Applications with SAS and R Alternative 9780367277321 DEWEY 006.31 Audience General Imprint Chapman & Hall/CRC We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:135119878;
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ISBN-13: 9780367538828
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