Technique | DecisionTrees J4.8 | NeuralNetworks | NaiveBayes/BayesNets | Instance-Based IB1/IBk/LR/LWR | GeneticAlgorithms | RuleLearning JRip/Foil |
Code (Weka/mine/other/adapted) | ||||||
Dataset (name): | ||||||
Accuracy (or error metrics). List metrics used. | ||||||
Stat. significantly better than: (list methods) | ||||||
Size of the model | ||||||
How readable is the model? | ||||||
Number of attributes used | ||||||
Num. of training instances | ||||||
Num. of test instances | ||||||
Missing values included?(y/n) | ||||||
What Pre-processing done? | ||||||
Evaluation method used (n-fold cross val with n=?) |
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Training Time | ||||||
Testing Time | ||||||
Strengths and Weaknesses |
For those methods for which two or more alternatives are listed on the table (e.g., Naive Bayes / Bayesian Nets), provide the required information for each of the alternative listed, in the order they are listed, separated by "/"s (e.g. "78% / 81%", under accuracy if the accuracy of your best Naive Bayes model was 78% and the accuracy of your best Bayesian Net was 81% on the dataset analyzed).
Include in your written report detail description and analysis of your table.