| Description |
xiii, 600 pages : illustrations (some color) ; 24 cm |
| Contents |
General Strategies. A Short Tour of the Predictive Modeling Process -- Data Pre-processing -- Over-Fitting and Model Tuning -- Regression Models. Measuring Performance in Regression Models -- Linear Regression and Its Cousins -- Nonlinear Regression Models -- Regression Trees and Rule-Based Models -- A Summary of Solubility Models -- Case Study: Compressive Strength of Concrete Mixtures -- Classification Models. Measuring Performance in Classification Models -- Discriminant Analysis and Other Linear Classification Models -- Nonlinear Classification Models -- Classification Trees and Rule-Based Models -- A Summary of Grant Application Models -- Remedies for Severe Class Imbalance -- Case Study: Job Scheduling -- Other Considerations. Measuring Predictor Importance -- An Introduction to Feature Selection -- Factors That Can Affect Model Performance |
| Summary |
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. -- from back cover |
| Notes |
Includes index |
| Bibliography |
Includes bibliographical references (pages 569-587)and indexes |
| Subject |
Mathematical models.
|
|
Mathematical statistics.
|
|
Prediction theory.
|
| Reading List |
MIS373 recommended text 2024
|
| Author |
Johnson, Kjell.
|
| LC no. |
2013933452 |
| ISBN |
1461468485 |
|
9781461468486 |
|
