In response to Issue #67: Naive Bayes Classifier added along with a unit testing file; also, included a test_use_cases.py file to compare the accuracy of naive bayes models using both numpy-ml and scikit-learn using dataset in the file wine.data

[rishabh-jain14]

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[ddbourgin]

Hi Rishabh, thanks for the PR, and sorry for my delayed response. Also apologies for not closing issue #67 after @sfsf9797 's PR. I suspect this may have created some confusion :-/

Can you explain what your code adds over the existing implementation? I haven't gone through it in detail, but given that there already exists a validated Gaussian naive Bayes model in the repo, I'm inclined to leave things as they are unless there is significant added functionality.

[rishabh-jain14]

Hey David, thanks for the response.

The PR focuses on the implementation and comparison of the Gaussian Naive Bayes Classifier using the NumPy-ml repo as well as the scikit-learn library.

I had realized that the repo already contains a code for the Gaussian naive Bayes model but as per the issue mentioned #67, it required a brief coded file that focused on the comparison of the performance of the model in comparison to the already specified scikit-learn model. So, we can say that the comparison part is the significant added functionality here.

Feel free to include the PR into the repo if you'd like. I enjoyed assessing the entire repository and look forward to contributing as much as I can. :-)

Rishabh Jain

On Thu, Sep 23, 2021 at 9:04 PM David Bourgin @.***> wrote:

Hi Rishabh, thanks for the PR, and sorry for my delayed response. Also apologies for not closing issue #67 https://github.com/ddbourgin/numpy-ml/issues/67 after @sfsf9797 https://github.com/sfsf9797 's PR. I suspect this may have created some confusion :-/

Can you explain what your code adds over the existing implementation? I haven't gone through it in detail, but given that there already exists a validated Gaussian naive Bayes model in the repo, I'm inclined to leave things as they are unless there is significant added functionality.

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