Given that I have a deep learning model(handover from former colleague). For some reason, the train/dev set was missing.
In my situation, I want to classify my dataset into 100 categories. The dataset is extremely imbalanced. The dataset size is about tens of millions
First of all, I run the model and got the prediction on the whole dataset.
Then, I sample 100 records per category(according to the prediction) and got a 10,000 test set.
Next, I labeled the ground truth of each record for the test set and calculate the precision, recall, f1 for each category and got F1-micro and F1-macro.
How to estimate the accuracy or other metrics on the whole dataset? Is it correct that I use the weighted sum of each category's precision(the weight is the proportion of prediction on the whole) to estimate?