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Lead Tomorrow.  \nActuarial & Financial Risk Resource Materials  \nSince 1972  \nCopyright © 2025, ACTEX Learning, a division of ArchiMedia Advantage Inc.  \nNo portion of this ACTEX Study Manual may be reproduced or transmitted in any part or by any means without the permission of the publisher.  \nContents  \nPreface xi  \nP.1 About Exam ATPA .................................. xi  \nP.2 About this Study Manual .............................. xvi  \nI Advanced Predictive Analytics Models and Related Issues 1  \n1 Advanced Predictive Analytics Models 3  \n1.1 Generalized Additive Models ............................. 4  \n1.1.1 Conceptual Foundations ........................... 4  \n1.1.2 Case Study .................................. 7  \nTASK 1: Explore and prepare the data ................... 10  \nTASK 2: Fit and evaluate two GAMs .................... 17  \nTASK 3: Fine-tune and validate the recommended GAM ......... 26  \n1.2 Generalized Linear Mixed Models .......................... 31  \n1.2.1 Conceptual Foundations ........................... 31  \n1.2.2 Case Study .................................. 38  \nTASK 1: Explore and prepare the data ................... 39  \nTASK 2: Fit and evaluate a random intercepts GLMM .......... 43  \nTASK 3: Fit and evaluate a random slopes GLMM ............ 48  \nTASK 4: Use GLMMs to make predictions ................. 51  \n1.3 Neural Networks ................................... 54  \n1.3.1 Conceptual Foundations ........................... 54  \nPart 1: Basic Terminology .......................... 55  \nPart 2: How to Train a Neural Network ................... 62  \n1.3.2 Case Study 1: Regression .......................... 68  \nTASK 1: Prepare the data .......................... 69  \nTASK 2: Build and evaluate a baseline neural network .......... 72  \nTASK 3: Explore the effects of the mini-batch size and learning rate ... 77  \nTASK 4: Tune and evaluate the neural network .............. 81  \n1.3.3 Case Study 2: Classification ......................... 89  \nTASK 1: Explore the data .......................... 90  \nTASK 2: Build and evaluate the first network classifier .......... 93  \nTASK 3: Tune and evaluate the network .................. 100  \nTASK 4: Tune and evaluate the network .................. 105  \n1.4 Bayesian Regression ................................. 108  \n1.4.1 Conceptual Foundations ........................... 108  \n1.4.2 Case Study .................................. 113  \nTASK 1: Fit and analyze a Bayesian GLM ................. 113  \nTASK 2: Compare models and make predictions .............. 120  \n1.5 Stacking ........................................ 123  \n1.5.1 Conceptual Foundations ........................... 123  \n1.5.2 Case Study .................................. 128  \nTASK 1: Build the stage-0 models ..................... 128  \nTASK 2: Tune the stage-1 model ...................... 132  \n2 Model Explainability and Communication 137  \n2.1 Techniques for Interpreting Opaque Models ..................... 138  \n2.1.1 Global Method 1: Variable Importance ................... 140  \n2.1.2 Global Method 2: Partial Dependence ................... 144  \n2.1.3 Global Method 3: Global Surrogate Models ................ 152  \n2.1.4 Local Method 1: ICE Plots ......................... 153  \n2.1.5 Local Method 2: SHAP ........................... 155  \n2.1.6 Digression: Lift and Gain Charts ...................... 156  \n2.2 Techniques for Communicating with the Intended Audience ............ 162  \n2.2.1 Technical Reports ............................... 165  \n2.2.2 Executive Summaries ............................. 168  \nII Advanced Data Management 173  \n3 Advanced Data Management: Technical Issues 175  \n3.1 Data Pipeline ..................................... 176  \n3.2 Data Transformation ................................. 178  \n3.2.1 Data Manipulation on Rows ......................... 180  \n3.2.2 Data Manipulation on Columns ....................... 188  \n3.2.3 Grouping and Summarizing Da","cbCaibPP3BlEIUdH","https://ap.wps.com/l/cbCaibPP3BlEIUdH","pdf",5980910,63,"English","# Preface\n## About Exam ATPA\n## About this Study Manual\n# Advanced Predictive Analytics Models and Related Issues\n## Advanced Predictive Analytics Models\n### Generalized Additive Models\n### Generalized Linear Mixed Models\n### Neural Networks\n### Bayesian Regression\n### Stacking\n# Model Explainability and Communication\n## Techniques for Interpreting Opaque Models\n## Techniques for Communicating with the Intended Audience\n# Advanced Data Management\n## Advanced Data Management: Technical Issues\n## Advanced Data Management: Ethical and Legal Issues","[{\"question\":\"What modeling topics are covered for Exam ATPA preparation?\",\"answer\":\"The manual covers generalized additive models, generalized linear mixed models, neural networks, Bayesian regression, and stacking, with case studies and structured tasks to fit, evaluate, tune, and validate models.\"},{\"question\":\"How does the book approach model explainability?\",\"answer\":\"It presents techniques for interpreting opaque models, including global and local methods such as variable importance, partial dependence, surrogate models, ICE plots, and SHAP, along with related charts like lift and gain.\"},{\"question\":\"What data management and compliance areas are included?\",\"answer\":\"It covers technical issues such as data pipelines, transformations, cleaning, and validation, plus ethical principles, regulations and standards of practice, and algorithmic fairness considerations.\"}]","ACTEX Study Manual for Exam ATPA - 2nd Edition - Advanced Predictive Analytics Models and Related Issues | PDF",1790281232,159]