Technique | DecisionTrees ID3/J4.8 | NeuralNetworks | NaiveBayes/BayesNets | Instance-Based IB1/IBk/LBR/LWR | GeneticAlgorithms | RuleLearning Prism/Foil | Technique re-done for final project: ________ |
Code (mine/other/adapted) | |||||||
Programming Language | |||||||
DATASET I (name): | |||||||
Best accuracy | |||||||
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) | |||||||
Pre-processing done | |||||||
Evaluation method used (n-fold cross val, % split, ...) |
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Training Time | |||||||
Testing Time | |||||||
DATASET II (name), if any: | |||||||
Best accuracy | |||||||
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) | |||||||
Pre-processing done | |||||||
Evaluation method used (n-fold cross val, % split, ...) |
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Training Time | |||||||
Testing Time | |||||||
Domains-for/Conditions-under which this method works best | |||||||
Strengths of this project/column | |||||||
Weaknesses of this project/column | |||||||
Other comments |
Your report should contain the following sections with the corresponding discussions:
[your-lastname]_proj9_slides.[ext] containing your slides for your oral report. This file should be either a PDF file (ext=pdf) or a PowerPoint file (ext=ppt). Please use only lower case letters in the name file. For instance my file would be named ruiz_proj9_slides.ppt