About the optimization of the Naive Bayes classifier.

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About the optimization of the Naive Bayes classifier.

Liming Tan
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Re: About the optimization of the Naive Bayes classifier.

Eibe Frank-2
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There is no very convenient way to do this in WEKA using a single train/validation split. However, you can use MultiScheme (https://weka.sourceforge.io/doc.stable-3-8/weka/classifiers/meta/MultiScheme.html) to implement selection using k-fold cross-validation (on the training set). This will be more robust anyway and is generally preferable unless the dataset is so large that k-fold cross-validation becomes too expensive..

Cheers,
Eibe

On Fri, Apr 30, 2021 at 11:22 AM Liming Tan <[hidden email]> wrote:
Hello!

A paper I read recently mentioned the use of the open-source toolkit WEKA. 

Three data sets are used in the paper: training set, development set, and test set. The classifier chosen is a Naive Bayes classifier.

The original paper contains this sentence:
"The parameters of the classifier (using kernel density or normal estimator) are optimised on the development set and applied to the test set."

But I don't find the option to use the development set in WEKA's Explorer.
Does this mean that the development set is merged into the training set?

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Re: About the optimization of the Naive Bayes classifier.

Liming Tan
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Re: About the optimization of the Naive Bayes classifier.

Eibe Frank-3
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