Ensemble methods in data mining: improving accuracy through combining predictions
1. Ensembles discovered -- Building ensembles -- Regularization -- Real-world examples: credit scoring + the Netflix challenge -- Organization of this book --
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[San Rafael, California]
Morgan & Claypool Publishers
[2010]
|
| Series: | Synthesis lectures on data mining and knowledge discovery
#2 |
| Subjects: | |
| Online Access: | UER01 Volltext |
| Summary: | 1. Ensembles discovered -- Building ensembles -- Regularization -- Real-world examples: credit scoring + the Netflix challenge -- Organization of this book -- 2. Predictive learning and decision trees -- Decision tree induction overview -- Decision tree properties -- Decision tree limitations -- 3. Model complexity, model selection and regularization -- What is the "right" size of a tree -- Bias-variance decomposition -- Regularization -- Regularization and cost-complexity tree pruning -- Cross-validation -- Regularization via shrinkage -- Regularization via incremental model building -- Example -- Regularization summary -- |
| Physical Description: | 1 Online-Ressource (xvi, 108 Seiten) |
| ISBN: | 9781608452859 |
| DOI: | 10.2200/S00240ED1V01Y200912DMK002 |
Staff View
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| 505 | 8 | |a Acknowledgments; Foreword by Jaffray Woodriff; Foreword by Tin Kam Ho; Ensembles Discovered; Building Ensembles; Regularization; Real-World Examples: Credit Scoring + the Netflix Challenge; Organization of This Book; Predictive Learning and Decision Trees; Decision Tree Induction Overview; Decision Tree Properties; Decision Tree Limitations; Model Complexity, Model Selection and Regularization; What is the ''Right'' Size of a Tree?; Bias-Variance Decomposition; Regularization; Regularization and Cost-Complexity Tree Pruning; Cross-Validation; Regularization via Shrinkage | |
| 505 | 8 | |a Regularization via Incremental Model BuildingExample; Regularization Summary; Importance Sampling and the Classic Ensemble Methods; Importance Sampling; Parameter Importance Measure; Perturbation Sampling; Generic Ensemble Generation; Bagging; Example; Why it Helps?; Random Forest; AdaBoost; Example; Why the Exponential Loss?; AdaBoost's Population Minimizer; Gradient Boosting; MART; Parallel vs. Sequential Ensembles; Rule Ensembles and Interpretation Statistics; Rule Ensembles; Interpretation; Simulated Data Example; Variable Importance; Partial Dependences; Interaction Statistic | |
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| contents | Acknowledgments; Foreword by Jaffray Woodriff; Foreword by Tin Kam Ho; Ensembles Discovered; Building Ensembles; Regularization; Real-World Examples: Credit Scoring + the Netflix Challenge; Organization of This Book; Predictive Learning and Decision Trees; Decision Tree Induction Overview; Decision Tree Properties; Decision Tree Limitations; Model Complexity, Model Selection and Regularization; What is the ''Right'' Size of a Tree?; Bias-Variance Decomposition; Regularization; Regularization and Cost-Complexity Tree Pruning; Cross-Validation; Regularization via Shrinkage Regularization via Incremental Model BuildingExample; Regularization Summary; Importance Sampling and the Classic Ensemble Methods; Importance Sampling; Parameter Importance Measure; Perturbation Sampling; Generic Ensemble Generation; Bagging; Example; Why it Helps?; Random Forest; AdaBoost; Example; Why the Exponential Loss?; AdaBoost's Population Minimizer; Gradient Boosting; MART; Parallel vs. Sequential Ensembles; Rule Ensembles and Interpretation Statistics; Rule Ensembles; Interpretation; Simulated Data Example; Variable Importance; Partial Dependences; Interaction Statistic Manufacturing Data ExampleSummary; Ensemble Complexity; Complexity; Generalized Degrees of Freedom; Examples: Decision Tree Surface with Noise; R Code for GDF and Example; Summary and Discussion; AdaBoost Equivalence to FSF Procedure; Gradient Boosting and Robust Loss Functions; Bibliography; Authors' Biographies; |
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| id | DE-604.BV044754832 |
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| indexdate | 2024-07-10T08:01:18Z |
| institution | BVB |
| isbn | 9781608452859 |
| language | English |
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| physical | 1 Online-Ressource (xvi, 108 Seiten) |
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| series2 | Synthesis lectures on data mining and knowledge discovery |
| spelling | Seni, Giovanni Verfasser (DE-588)1036432947 aut Ensemble methods in data mining improving accuracy through combining predictions Giovanni Seni (Elder Research, Inc. and Santa Clara University), John F. Elder (Elder Research, Inc. and University of Virginia) [San Rafael, California] Morgan & Claypool Publishers [2010] © 2010 1 Online-Ressource (xvi, 108 Seiten) txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on data mining and knowledge discovery #2 Acknowledgments; Foreword by Jaffray Woodriff; Foreword by Tin Kam Ho; Ensembles Discovered; Building Ensembles; Regularization; Real-World Examples: Credit Scoring + the Netflix Challenge; Organization of This Book; Predictive Learning and Decision Trees; Decision Tree Induction Overview; Decision Tree Properties; Decision Tree Limitations; Model Complexity, Model Selection and Regularization; What is the ''Right'' Size of a Tree?; Bias-Variance Decomposition; Regularization; Regularization and Cost-Complexity Tree Pruning; Cross-Validation; Regularization via Shrinkage Regularization via Incremental Model BuildingExample; Regularization Summary; Importance Sampling and the Classic Ensemble Methods; Importance Sampling; Parameter Importance Measure; Perturbation Sampling; Generic Ensemble Generation; Bagging; Example; Why it Helps?; Random Forest; AdaBoost; Example; Why the Exponential Loss?; AdaBoost's Population Minimizer; Gradient Boosting; MART; Parallel vs. Sequential Ensembles; Rule Ensembles and Interpretation Statistics; Rule Ensembles; Interpretation; Simulated Data Example; Variable Importance; Partial Dependences; Interaction Statistic Manufacturing Data ExampleSummary; Ensemble Complexity; Complexity; Generalized Degrees of Freedom; Examples: Decision Tree Surface with Noise; R Code for GDF and Example; Summary and Discussion; AdaBoost Equivalence to FSF Procedure; Gradient Boosting and Robust Loss Functions; Bibliography; Authors' Biographies; 1. Ensembles discovered -- Building ensembles -- Regularization -- Real-world examples: credit scoring + the Netflix challenge -- Organization of this book -- 2. Predictive learning and decision trees -- Decision tree induction overview -- Decision tree properties -- Decision tree limitations -- 3. Model complexity, model selection and regularization -- What is the "right" size of a tree -- Bias-variance decomposition -- Regularization -- Regularization and cost-complexity tree pruning -- Cross-validation -- Regularization via shrinkage -- Regularization via incremental model building -- Example -- Regularization summary -- Data mining Electronic books Elder, John F. 1961- Verfasser (DE-588)140491279 aut Erscheint auch als Druck-Ausgabe, Paperback 978-1-60845-284-2 Synthesis lectures on data mining and knowledge discovery #2 (DE-604)BV044754814 2 https://doi.org/10.2200/S00240ED1V01Y200912DMK002 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Seni, Giovanni Elder, John F. 1961- Ensemble methods in data mining improving accuracy through combining predictions Synthesis lectures on data mining and knowledge discovery Acknowledgments; Foreword by Jaffray Woodriff; Foreword by Tin Kam Ho; Ensembles Discovered; Building Ensembles; Regularization; Real-World Examples: Credit Scoring + the Netflix Challenge; Organization of This Book; Predictive Learning and Decision Trees; Decision Tree Induction Overview; Decision Tree Properties; Decision Tree Limitations; Model Complexity, Model Selection and Regularization; What is the ''Right'' Size of a Tree?; Bias-Variance Decomposition; Regularization; Regularization and Cost-Complexity Tree Pruning; Cross-Validation; Regularization via Shrinkage Regularization via Incremental Model BuildingExample; Regularization Summary; Importance Sampling and the Classic Ensemble Methods; Importance Sampling; Parameter Importance Measure; Perturbation Sampling; Generic Ensemble Generation; Bagging; Example; Why it Helps?; Random Forest; AdaBoost; Example; Why the Exponential Loss?; AdaBoost's Population Minimizer; Gradient Boosting; MART; Parallel vs. Sequential Ensembles; Rule Ensembles and Interpretation Statistics; Rule Ensembles; Interpretation; Simulated Data Example; Variable Importance; Partial Dependences; Interaction Statistic Manufacturing Data ExampleSummary; Ensemble Complexity; Complexity; Generalized Degrees of Freedom; Examples: Decision Tree Surface with Noise; R Code for GDF and Example; Summary and Discussion; AdaBoost Equivalence to FSF Procedure; Gradient Boosting and Robust Loss Functions; Bibliography; Authors' Biographies; |
| title | Ensemble methods in data mining improving accuracy through combining predictions |
| title_auth | Ensemble methods in data mining improving accuracy through combining predictions |
| title_exact_search | Ensemble methods in data mining improving accuracy through combining predictions |
| title_full | Ensemble methods in data mining improving accuracy through combining predictions Giovanni Seni (Elder Research, Inc. and Santa Clara University), John F. Elder (Elder Research, Inc. and University of Virginia) |
| title_fullStr | Ensemble methods in data mining improving accuracy through combining predictions Giovanni Seni (Elder Research, Inc. and Santa Clara University), John F. Elder (Elder Research, Inc. and University of Virginia) |
| title_full_unstemmed | Ensemble methods in data mining improving accuracy through combining predictions Giovanni Seni (Elder Research, Inc. and Santa Clara University), John F. Elder (Elder Research, Inc. and University of Virginia) |
| title_short | Ensemble methods in data mining |
| title_sort | ensemble methods in data mining improving accuracy through combining predictions |
| title_sub | improving accuracy through combining predictions |
| url | https://doi.org/10.2200/S00240ED1V01Y200912DMK002 |
| volume_link | (DE-604)BV044754814 |
| work_keys_str_mv | AT senigiovanni ensemblemethodsindataminingimprovingaccuracythroughcombiningpredictions AT elderjohnf ensemblemethodsindataminingimprovingaccuracythroughcombiningpredictions |