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Wait, you had 15 test cases, and didn't even say how the classifier did? I guess I could run the code myself, and that's nice you provided it on Github, but sti

Wait, you had 15 test cases, and didn't even say how the classifier did? I guess I could run the code myself, and that's nice you provided it on Github, but still, I feel like you gotta post results in a blog post like this.

ioloop 10y ago
You are indeed right, and I encourage anyone interested in computing the precision metrics to do so. The focus of this post is the GoLang proxy used for the caching. It's actually used in a CI / CD environment, but I'm finding it incredibly useful for a whole variety of tasks. Regarding the precision metrics, you can find all the information required here http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html http://scikit-learn.org/stable/tutorial/text_analytics/worki... to compute the precision. In the scikit-learn article the classifier scores over 90% precision, so I'd expect it would be possible to do the same. I'll be posting more about this over the xmas period, so I'll write a part II, where I compute the precision metrics. Another thing worth noting is that it's not caching the HN comments, while it is using the reddit comments. Despite using tfidf, this still completely skews the results towards reddit as opposed to HN. So that's something else any interested reader can look into. Thank you for pointing this out.
- jackschultz 10y ago
  Well sure it's possible, I've done writeups on text classification with Scikit-Learn as well here: https://bigishdata.com/2016/12/05/classifying-amazon-reviews-with-scikit-learn-more-data-is-better-turns-out/ https://bigishdata.com/2016/12/05/classifying-amazon-reviews... But results and how well the classifier performs really just depends on the quality and amount of training data you have. So would be interesting to see how this does if you can get a bunch more data from each of the subreddits and have some more test examples!
  - ioloop 10y ago
    Thank you Jack. I think your point is important so I'll add a note at the end of the article. I'll also add link to your article as I think it's complimentary.

ioloop 10y ago

You are indeed right, and I encourage anyone interested in computing the precision metrics to do so. The focus of this post is the GoLang proxy used for the caching. It's actually used in a CI / CD environment, but I'm finding it incredibly useful for a whole variety of tasks. Regarding the precision metrics, you can find all the information required here http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html http://scikit-learn.org/stable/tutorial/text_analytics/worki... to compute the precision. In the scikit-learn article the classifier scores over 90% precision, so I'd expect it would be possible to do the same. I'll be posting more about this over the xmas period, so I'll write a part II, where I compute the precision metrics. Another thing worth noting is that it's not caching the HN comments, while it is using the reddit comments. Despite using tfidf, this still completely skews the results towards reddit as opposed to HN. So that's something else any interested reader can look into. Thank you for pointing this out.

jackschultz 10y ago
Well sure it's possible, I've done writeups on text classification with Scikit-Learn as well here: https://bigishdata.com/2016/12/05/classifying-amazon-reviews-with-scikit-learn-more-data-is-better-turns-out/ https://bigishdata.com/2016/12/05/classifying-amazon-reviews... But results and how well the classifier performs really just depends on the quality and amount of training data you have. So would be interesting to see how this does if you can get a bunch more data from each of the subreddits and have some more test examples!
- ioloop 10y ago
  Thank you Jack. I think your point is important so I'll add a note at the end of the article. I'll also add link to your article as I think it's complimentary.