From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods
Description
Ensemble methods are one of the most influential developments in Machine Learning over the past decade. They perform extremely well in a variety of problem domains, have desirable statistical properties, and scale well computationally. By combining competing models into a committee, they can strengthen “weak” learning procedures. This tutorial is aimed at both novice and advanced data mining researchers and practitioners especially in Engineering, Statistics, and Computer Science. Users with little exposure to ensemble methods will gain a clear overview of each method. Advanced practitioners already employing ensembles will gain insight into this breakthrough way to create next-generation models.
Categories
Top: Computer Science: Data MiningTop: Computer Science: Machine Learning: Ensemble Methods
| Slides | |
| 0:00 | From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods |
| 1:14 | Tutorial Goals |
| 2:00 | Overview |
| 4:09 | Overview (2) |
| 4:54 | Ensemble Methods in a Nutshell |
| 6:58 | Ensemble Methods In a Nutshell (2) |
| 8:20 | Examples - Data Mining Products |
| 8:57 | Examples - Algorithm Response Surfaces |
| 11:07 | Examples - Relative Performance |
| 13:12 | Examples - “Bundling” Improves Performance |
| 14:32 | Examples - Relative Performance |
| 14:54 | Examples - “Bundling” Improves Performance |
| 15:03 | Examples - Credit Scoring Performace |
| 18:38 | Timeline |
| 20:31 | Timeline (2) |
| 24:19 | Overview - Predictive Learning |
| 24:23 | Predictive Learning - Example |
| 25:20 | Predictive Learning - Example (2) |
| 26:33 | Predictive Learning - Example (3) |
| 27:55 | Predictive Learning - Procedure Summary |
| 29:05 | Predictive Learning - Procedure Summary (2) |
| 30:07 | Overview - Decision Trees |
| 30:09 | Decision Trees - Induction Overview |
| 30:50 | Decision Trees - Induction Overview (2) |
| 31:18 | Decision Trees - Induction Overview (3) |
| 32:09 | Decision Trees - Growing Algorithm |
| 33:40 | Decision Trees - Desirable Data Mining Properties |
| 34:36 | Decision Trees - Desirable Data Mining Properties (2) |
| 35:40 | Decision Trees - Limitations |
| 36:03 | Decision Trees - Limitations (2) |
| 36:31 | Decision Trees - Limitations (3) |
| 37:42 | Overview - Model Selection |
| 40:49 | Model Selection - What is the “right” size of a tree? |
| 42:48 | Model Selection - Bias−Variance Decomposition |
| 46:19 | Model Selection - Bias−Variance Schematic |
| 48:53 | Model Selection - Bias−Variance Tradeoff |
| 50:31 | Model Selection - Tree Pruning |
| 53:03 | Model Selection - Tree Pruning (2) |
| 55:10 | Model Selection - Cross−Validation |
| 56:43 | Model Selection - Cross−Validation (2) |
| 57:47 | Model Selection - Cross−Validation (3) |
| 58:01 | - Questions |
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