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Supervised Machine Learning: Optimization Framework And Applications With Sas And R,New
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Ai Framework Intended To Solve A Problem Of Biasvariance Tradeoff For Supervised Learning Methods In Reallife 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 Hyperparameters 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 Dataidentifying Optimal Feature Subsets And Hyperparameters Of Ml Methods Can Be Resolved Using Design And Analysis Of Statistical Experimentsusing A Bootstrapping Approach To Massive Sampling Of Training And Tests Datasets With Various Data Characteristics (E.G.: Contaminated Training Sets) Allows Dealing With Biasdeveloping Of Sasbased Tabledriven Environment Allows Managing All Metadata Related To The Proposed Ai Framework And Creating Interoperability With R Libraries To Accomplish Variety Of Statistical And Machinelearning Taskscomputer Programs In R And Sas That Create Ai Framework Are Available On Github
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