Automated Machine Learning for Business
-10%
portes grátis
Automated Machine Learning for Business
R. Larsen, Kai; Becker, Daniel S.
Oxford University Press Inc
10/2021
352
Mole
Inglês
9780190941666
15 a 20 dias
578
Descrição não disponível.
Preface
Section I: Why Use Automated Machine Learning?
Chapter 1: What is Machine Learning?
Chapter 2: Automating Machine Learning
Section II: Defining Project Objectives
Chapter 3: Specify Business Problem
Chapter 4: Acquire Subject Matter Expertise
Chapter 5: Define Prediction Target
Chapter 6: Decide on Unit of Analysis
Chapter 7: Success, Risk, and Continuation
Section III: Acquire and Integrate Data
Chapter 8: Accessing and Storing Data
Chapter 9: Data Integration
Chapter 10: Data Transformations
Chapter 11: Summarization
Chapter 12: Data Reduction and Splitting
Section IV: Model Data
Chapter 13: Startup Processes
Chapter 14: Feature Understanding and Selection
Chapter 15: Build Candidate Models
Chapter 16: Understanding the Process
Chapter 17: Evaluate Model Performance
Chapter 18: Comparing Model Pairs
Chapter 19: Interpret Model
Chapter 20: Communicate Model Insights
Section VI: Implement, Document, and Maintain
Chapter 21: Set Up Prediction System
Chapter 22: Document Modeling Process for Reproducibility
Chapter 23: Create Model Monitoring and Maintenance Plan
Chapter 24: Seven Types of Target Leakage in Machine Learning and an Exercise
Chapter 25: Time-Aware Modeling
Chapter 26: Time-Series Modeling
References
Appendix A: Datasets
Appendix B: Optimization and Sorting Measures
Appendix C: More on Cross Variation
Section I: Why Use Automated Machine Learning?
Chapter 1: What is Machine Learning?
Chapter 2: Automating Machine Learning
Section II: Defining Project Objectives
Chapter 3: Specify Business Problem
Chapter 4: Acquire Subject Matter Expertise
Chapter 5: Define Prediction Target
Chapter 6: Decide on Unit of Analysis
Chapter 7: Success, Risk, and Continuation
Section III: Acquire and Integrate Data
Chapter 8: Accessing and Storing Data
Chapter 9: Data Integration
Chapter 10: Data Transformations
Chapter 11: Summarization
Chapter 12: Data Reduction and Splitting
Section IV: Model Data
Chapter 13: Startup Processes
Chapter 14: Feature Understanding and Selection
Chapter 15: Build Candidate Models
Chapter 16: Understanding the Process
Chapter 17: Evaluate Model Performance
Chapter 18: Comparing Model Pairs
Chapter 19: Interpret Model
Chapter 20: Communicate Model Insights
Section VI: Implement, Document, and Maintain
Chapter 21: Set Up Prediction System
Chapter 22: Document Modeling Process for Reproducibility
Chapter 23: Create Model Monitoring and Maintenance Plan
Chapter 24: Seven Types of Target Leakage in Machine Learning and an Exercise
Chapter 25: Time-Aware Modeling
Chapter 26: Time-Series Modeling
References
Appendix A: Datasets
Appendix B: Optimization and Sorting Measures
Appendix C: More on Cross Variation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Preface
Section I: Why Use Automated Machine Learning?
Chapter 1: What is Machine Learning?
Chapter 2: Automating Machine Learning
Section II: Defining Project Objectives
Chapter 3: Specify Business Problem
Chapter 4: Acquire Subject Matter Expertise
Chapter 5: Define Prediction Target
Chapter 6: Decide on Unit of Analysis
Chapter 7: Success, Risk, and Continuation
Section III: Acquire and Integrate Data
Chapter 8: Accessing and Storing Data
Chapter 9: Data Integration
Chapter 10: Data Transformations
Chapter 11: Summarization
Chapter 12: Data Reduction and Splitting
Section IV: Model Data
Chapter 13: Startup Processes
Chapter 14: Feature Understanding and Selection
Chapter 15: Build Candidate Models
Chapter 16: Understanding the Process
Chapter 17: Evaluate Model Performance
Chapter 18: Comparing Model Pairs
Chapter 19: Interpret Model
Chapter 20: Communicate Model Insights
Section VI: Implement, Document, and Maintain
Chapter 21: Set Up Prediction System
Chapter 22: Document Modeling Process for Reproducibility
Chapter 23: Create Model Monitoring and Maintenance Plan
Chapter 24: Seven Types of Target Leakage in Machine Learning and an Exercise
Chapter 25: Time-Aware Modeling
Chapter 26: Time-Series Modeling
References
Appendix A: Datasets
Appendix B: Optimization and Sorting Measures
Appendix C: More on Cross Variation
Section I: Why Use Automated Machine Learning?
Chapter 1: What is Machine Learning?
Chapter 2: Automating Machine Learning
Section II: Defining Project Objectives
Chapter 3: Specify Business Problem
Chapter 4: Acquire Subject Matter Expertise
Chapter 5: Define Prediction Target
Chapter 6: Decide on Unit of Analysis
Chapter 7: Success, Risk, and Continuation
Section III: Acquire and Integrate Data
Chapter 8: Accessing and Storing Data
Chapter 9: Data Integration
Chapter 10: Data Transformations
Chapter 11: Summarization
Chapter 12: Data Reduction and Splitting
Section IV: Model Data
Chapter 13: Startup Processes
Chapter 14: Feature Understanding and Selection
Chapter 15: Build Candidate Models
Chapter 16: Understanding the Process
Chapter 17: Evaluate Model Performance
Chapter 18: Comparing Model Pairs
Chapter 19: Interpret Model
Chapter 20: Communicate Model Insights
Section VI: Implement, Document, and Maintain
Chapter 21: Set Up Prediction System
Chapter 22: Document Modeling Process for Reproducibility
Chapter 23: Create Model Monitoring and Maintenance Plan
Chapter 24: Seven Types of Target Leakage in Machine Learning and an Exercise
Chapter 25: Time-Aware Modeling
Chapter 26: Time-Series Modeling
References
Appendix A: Datasets
Appendix B: Optimization and Sorting Measures
Appendix C: More on Cross Variation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
