File:Alperel-1.jpg
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English: Using Iris data set, I created a training and a test sets. I have set random _state to zero so the model is not going to output different results every time. I trained the model on the training test and test the model on the Testing set. Using Naive Bayes method, the resulted accuracy was 93%. SVM method with linear kernel gave 97% accuracy while RBF kernel results in 95%. We can imply from those numbers that SVM method works better with Iris data-set overall. The reason may be predictors being somewhat dependent since Naive Bayes classifier assumes them as independent. Linear method giving a better accuracy than an RDF shows that data is linearly more separable rather than regionally. |
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| Source | Own work |
| Author | Alperel11 |
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I, the copyright holder of this work, hereby publish it under the following license:
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 04:44, 19 February 2019 | | 1,387 × 766 (215 KB) | Alperel11 (talk | contribs) | User created page with UploadWizard |
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