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Data Mining with Weka (3.5: Pruning decision trees)
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Classroom Contents
Data Mining with Weka
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- 1 Data Mining with Weka: Trailer
- 2 Data Mining with Weka (1.1: Introduction)
- 3 Data Mining with Weka (1.2: Exploring the Explorer)
- 4 Data Mining with Weka (1.3: Exploring datasets)
- 5 Data Mining with Weka (1.4: Building a classifier)
- 6 Data Mining with Weka (1.5: Using a filter )
- 7 Data Mining with Weka (1.6: Visualizing your data)
- 8 Data Mining with Weka (2.1: Be a classifier!)
- 9 Data Mining with Weka (2.2: Training and testing)
- 10 Data Mining with Weka (2.3: Repeated training and testing)
- 11 Data Mining with Weka (2.4: Baseline accuracy)
- 12 Data Mining with Weka (2.5: Cross-validation)
- 13 Data Mining with Weka (2.6: Cross-validation results)
- 14 Data Mining with Weka (3.1: Simplicity first!)
- 15 Data Mining with Weka (3.2: Overfitting)
- 16 Data Mining with Weka (3.3: Using probabilities)
- 17 Data Mining with Weka (3.4: Decision trees)
- 18 Data Mining with Weka (3.5: Pruning decision trees)
- 19 Data Mining with Weka (3.6: Nearest neighbor)
- 20 Data Mining with Weka (4.1: Classification boundaries)
- 21 Data Mining with Weka (4.2: Linear regression)
- 22 Data Mining with Weka (4.3: Classification by regression)
- 23 Data Mining with Weka (4.4: Logistic regression)
- 24 Data Mining with Weka (4.5: Support vector machines)
- 25 Data Mining with Weka (4.6: Ensemble learning)
- 26 Data Mining with Weka (5.1: The data mining process)
- 27 Data Mining with Weka (5.2: Pitfalls and pratfalls)
- 28 Data Mining with Weka (5.3: Data mining and ethics)
- 29 Data Mining with Weka (5.4: Summary)
