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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.
List 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.
About the author
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.
Summary
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.
