Re: About the optimization of the Naive Bayes classifier.

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 <[email protected]> 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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