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I am currently working on a product classification business case.

I have over 2 millions products from one source to classify in 23 categories. The input is the name of the product(ex:'Nike Air Max'). These products are already labeled. Whereas I can split my dataset into training and test set to compute the accuracy (which is 90%), I would like to predict categories of a whole new dataset, coming from an other source.

Once it will be done, I need to check products one by one to know if my classifier works well on this new dataset because it is not labeled and I really need to know the accuracy of prediction for this new dataset.

How can I know, the size of the sample coming from the new dataset (which has about 500k products) I need to check to be sure that my accuracy will have a small error margin?

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The unlabeled data will have to be labeled to calculate accuracy. How much data to label is an empirical question.

Here are questions to answer to help guide that process:

  • How close is the distribution of the unlabeled data to the training data?
  • What is good enough for project goals?
  • What is small should error margin be? Can it differ by class?
  • Which is more important precision or recall?

Then monitor model performance. In particular, the confusion matrix and decision boundaries between important and commonly confused categories.

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