Hi all I am a new to machine learning. I am trying to use ML to predict the classifications of social media comments from users (text).

There are 2 sets of data, data set one includes 5,000 comments (with 30,000 features) and

data set two includes 20,000 comments (with 90,000 features).

I am building Naive Bayes Classifier for two sets of data respectively , and there are 4 classifications.

May I ask about the minimum sample size on the two datasets? Thank you very much!

  • $\begingroup$ What criteria are you using to determine the sample size? Without specifying criteria there is no way to answer the question. $\endgroup$ – Michael Chernick Apr 6 '18 at 3:35
  • $\begingroup$ thank you Michael! Could I ask about what criteria should I consider? Is that meaning like "predictive power and "features ratio"? $\endgroup$ – Anna Apr 6 '18 at 3:47
  • $\begingroup$ I am not sure what it is for this application. To give you an idea if you are estimating a model parameter you might be generating a confidence interval the criterion would be the width of the confidence interval for a given confidence level. $\endgroup$ – Michael Chernick Apr 6 '18 at 3:53
  • 1
    $\begingroup$ Many thanks! Since I am really new in ML, I have found a formula from "Mail and Internet Surveys" (Dillman, 2000) which indicates that 880 sample from data set 1 (with 5,000 comments) and 1014 sample from data set 2 (with 20,000 comments), where the confidence level is 95%. But I do not know the theory can be applied to ML or not. Could I ask that is this credible in ML sampling? Moreover, I have set the target of the classifier's predictive power 75%. By using the previous no. of sample I can achieve it. Sorry for such questions because this is the first time for me being in touch with ML. $\endgroup$ – Anna Apr 6 '18 at 4:18

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