Difference between revisions of "Orange: Naive Bayes"
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| − | + | Klasifikasi probabilistik yang cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature. | |
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==Input== | ==Input== | ||
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Model: trained model | Model: trained model | ||
| − | Naive Bayes | + | Widget Naive Bayes mempelajari model Naive Bayesian dari data. Widget ini hanya berfungsi untuk task classification. |
[[File:NaiveBayes-stamped.png|center|200px|thumb]] | [[File:NaiveBayes-stamped.png|center|200px|thumb]] | ||
Revision as of 15:18, 6 March 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/naivebayes.html
Klasifikasi probabilistik yang cepat dan sederhana berdasarkan teorema Bayes dengan asumsi independensi feature.
Input
Data: input dataset Preprocessor: preprocessing method(s)
Output
Learner: naive bayes learning algorithm Model: trained model
Widget Naive Bayes mempelajari model Naive Bayesian dari data. Widget ini hanya berfungsi untuk task classification.
This widget has two options: the name under which it will appear in other widgets and producing a report. The default name is Naive Bayes. When you change it, you need to press Apply.
Contoh
Here, we present two uses of this widget. First, we compare the results of the Naive Bayes with another model, the Random Forest. We connect iris data from File to Test & Score. We also connect Naive Bayes and Random Forest to Test & Score and observe their prediction scores.
The second schema shows the quality of predictions made with Naive Bayes. We feed the Test & Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatter Plot with File. Then we select the misclassified instances in the Confusion Matrix and show feed them to Scatter Plot. The bold dots in the scatterplot are the misclassified instances from Naive Bayes.
