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Wikipedia:

• Weka (machine learning)

See also:

Machine Learning Meta Guide | WEKA & Dialog Systems 2017

[128x Apr 2018]

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• Weka Tutorial 38: Learning Curves 2 (Model Evaluation)
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• Weka Tutorial 37: Weighted Averages of Scores (Model Evaluation)
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• Weka Tutorial 36: Learning Curve 1 (Model Evaluation)
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• Weka Tutorial 35: Creating Training, Validation and Test Sets (Data Preprocessing)
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• Weka Tutorial 34: Generating Stratified Folds (Data Preprocessing)
• Weka Tutorial 33: Random Undersampling (Class Imbalance Problem)
• Weka Tutorial 32: Document classification 2 (Application)
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• Weka Tutorial 30: Multiple ROC Curves (Model Evaluation)
• Weka Tutorial 29: Precision-Recall Curve (Model Evaluation)
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• Weka Tutorial 28: ROC Curves and AUC (Model Evaluation)
• Weka Tutorial 27: Inverse k-fold Cross Validation (Model Evaluation)
• Weka Tutorial 26: Semi-supervised Learning (Learning Techniques)
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• Weka Tutorial 22: Setting Class Attribute (Data Preprocessing)
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• Weka Tutorial 20: Attribute Selection with Knowledge Flow Environment (Data Dimensionality)
• Weka Tutorial 19: Outliers and Extreme Values (Data Preprocessing)
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• Weka Tutorial 18: Classification 101 with Knowledge Flow Environment (Classification)
• Weka Tutorial 16: Detail Cross Validation Results using API (Model Evaluation)
• Weka Tutorial 17: Saving Results in Weka (Application)
• Weka Tutorial 15: Java API 101 (Application)
• Weka Tutorial 14: The Java API with Eclipse (Application)
• Weka Tutorial 13: Stacking Multiple Classifiers (Classification)
• Weka Tutorial 12: Cross Validation Error Rates (Model Evaluation)
• Weka Tutorial 11: Generating Non-stratified Folds (Data Preprocessing)
• Weka Tutorial 10: Feature Selection with Filter (Data Dimensionality)
• Weka Tutorial 09: Feature Selection with Wrapper (Data Dimensionality)
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• Weka Tutorial 08: Numeric Transform (Data Preprocessing)
• Weka Tutorial 07: Models 101 (Model Evaluation)
• Weka Tutorial 06: Discretization (Data Preprocessing)
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