Feature engineering and selection : a practical approach for predictive models /: a practical approach for predictive models. (2019)
- Record Type:
- Book
- Title:
- Feature engineering and selection : a practical approach for predictive models /: a practical approach for predictive models. (2019)
- Main Title:
- Feature engineering and selection : a practical approach for predictive models
- Further Information:
- Note: Max Kuhn, Kjell Johnson.
- Authors:
- Kuhn, Max
Johnson, Kjell - Contents:
- 1. Introduction; A Simple Example; Important Concepts; A More Complex Example; Feature Selection; An Outline of the Book; Computing 2. Illustrative Example: Predicting Risk of Ischemic Stroke; Splitting; Preprocessing; Exploration; Predictive Modeling Across Sets; Other Considerations; Computing 3. A Review of the Predictive Modeling Process; Illustrative Example: OkCupid Profile Data; Measuring Performance; Data Splitting; Resampling; Tuning Parameters and Overfitting; Model Optimization and Tuning; Comparing Models Using the Training Set; Feature Engineering Without Overfitting; Summary; Computing 4. Exploratory Visualizations ; Introduction to the Chicago Train Ridership Data; Visualizations for Numeric Data: Exploring Train Ridership Data; Visualizations for Categorical Data: Exploring the OkCupid Data; Post Modeling Exploratory Visualizations; Summary; Computing 5. Encoding Categorical Predictors ; Creating Dummy Variables for Unordered Categories; Encoding Predictors with Many Categories; Approaches for Novel Categories; Supervised Encoding Methods; Encodings for Ordered Data; Creating Features from Text Data; Factors versus Dummy Variables in Tree-Based Models; Summary; Computing 6. Engineering Numeric Predictors; Transformations; Many Transformations; Many: Many Transformations; Summary; Computing 7. Detecting Interaction Effects; Guiding Principles in the Search for Interactions; Practical Considerations; The Brute-Force Approach to Identifying Predictive1. Introduction; A Simple Example; Important Concepts; A More Complex Example; Feature Selection; An Outline of the Book; Computing 2. Illustrative Example: Predicting Risk of Ischemic Stroke; Splitting; Preprocessing; Exploration; Predictive Modeling Across Sets; Other Considerations; Computing 3. A Review of the Predictive Modeling Process; Illustrative Example: OkCupid Profile Data; Measuring Performance; Data Splitting; Resampling; Tuning Parameters and Overfitting; Model Optimization and Tuning; Comparing Models Using the Training Set; Feature Engineering Without Overfitting; Summary; Computing 4. Exploratory Visualizations ; Introduction to the Chicago Train Ridership Data; Visualizations for Numeric Data: Exploring Train Ridership Data; Visualizations for Categorical Data: Exploring the OkCupid Data; Post Modeling Exploratory Visualizations; Summary; Computing 5. Encoding Categorical Predictors ; Creating Dummy Variables for Unordered Categories; Encoding Predictors with Many Categories; Approaches for Novel Categories; Supervised Encoding Methods; Encodings for Ordered Data; Creating Features from Text Data; Factors versus Dummy Variables in Tree-Based Models; Summary; Computing 6. Engineering Numeric Predictors; Transformations; Many Transformations; Many: Many Transformations; Summary; Computing 7. Detecting Interaction Effects; Guiding Principles in the Search for Interactions; Practical Considerations; The Brute-Force Approach to Identifying Predictive Interactions; Approaches when Complete Enumeration is Practically Impossible; Other Potentially Useful Tools; Summary; Computing 8. Handling Missing Data; Understanding the Nature and Severity of Missing Information; Models that are Resistant to Missing Values; Deletion of Data; Encoding Missingness; Imputation methods; Special Cases; Summary; Computing 9. Working with Profile Data; Illustrative Data: Pharmaceutical Manufacturing Monitoring; What are the Experimental Unit and the Unit of Prediction?; Reducing Background; Reducing Other Noise; Exploiting Correlation; Impacts of Data Processing on Modeling; Summary; Computing 10. Feature Selection Overview; Goals of Feature Selection; Classes of Feature Selection Methodologies; Effect of Irrelevant Features; Overfitting to Predictors and External Validation; A Case Study; Next Steps; Computing 11. Greedy Search Methods; Illustrative Data: Predicting Parkinson’s Disease; Simple Filters; Recursive Feature Elimination; Stepwise Selection; Summary; Computing 12. Global Search Methods; Naive Bayes Models; Simulated Annealing; Genetic Algorithms; Test Set Results; Summary; Computing … (more)
- Edition:
- 1st
- Publisher Details:
- Boca Raton : Chapman & Hall/CRC
- Publication Date:
- 2019
- Extent:
- 1 online resource
- Subjects:
- 629.80285513
Predictive control -- Data processing
Predictive control -- Mathematical models
R (Computer program language) - Languages:
- English
- ISBNs:
- 9781351609463
9781351609470
9781351609456
9781315108230 - Related ISBNs:
- 9781138079229
- Notes:
- Note: Description based on CIP data; resource not viewed.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.446595
- Ingest File:
- 02_576.xml