Treers/metacostgithub.com
WebsetOptions public void setOptions(java.lang.String[] options) throws java.lang.Exception Parses a given list of options. Valid options are:-W classname Specify the full class name of a classifier (required).-C cost file WebPerformance (AUPRC) Text Processing. Apply Model (Documents) Dictionary-Based Sentiment (Documents) Extract Sentiment. Extract Topics from Data (LDA) Extract Topics from Documents (LDA) Filter Tokens Using ExampleSet. Split Document into Collection.
Treers/metacostgithub.com
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Web- MetaCost/MetaCost.py at master · Treers/MetaCost P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying c... WebDataFrame (load_iris (). data) >>> S ['target'] = load_iris (). target >>> LR = LogisticRegression (solver = 'lbfgs', multi_class = 'multinomial') >>> C = np. array ([[0, 1, 1], [1, 0, 1], [1, 1, 0]]) >>> model = MetaCost (S, LR, C). fit ('target', 3) >>> model. predict_proba (load_iris (). data …
WebMetaCost is a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. MetaCost is applicable to any number of classes and to arbitrary cost matrices. WebMetaCost operator: exactly the same as Bagging operator, with the addition of a cost matrix parameter --> use any base learner in learning process Multi-class prediction: Multi-class classification - classification involving more than two classes - some classification methods can handle multiple classes in a single model - for example, decision ...
WebMetaCost (Domingos, 1999) and Weighting (Ting, 1998). Section 3 describes Thresholding that can convert any cost-insensitive classifiers into cost-sensitive ones. The empirical evaluation is presented in Section 4, which is followed by conclusions in the last section. Review of Previous Work Cost-sensitive meta-learning converts existing cost- WebTree Game is officialy deprecated on GitHub. Please migrate your save to yhvr.gitlab.io/tree-game. Raise x to the power of 1.5. Cost: 2e4x Increase core production of x by 25. Cost: 2500x Begin production of x. Cost: Free Increase core production of x by 10. Cost: 100x Multiply x & y production by z 1/10. Cost: 1e3z Multiply x by 10. Cost: 1e6x
WebMetaCost is a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. …
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