Multi-label classification problem
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Predict as many tags as possible with high precision and recall. Incorrect tags could impact customer experience on StackOverflow. No strict latency constraints.
Refer: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data
All of the data is in 2 files: Train and Test. Train.csv contains 4 columns: Id,Title,Body,Tags. Test.csv contains the same columns but without the Tags, which you are to predict. Size of Train.csv - 6.75GB Size of Test.csv - 2GB Number of rows in Train.csv = 6034195
Dataset contains 6,034,195 rows. The columns in the table are: Id - Unique identifier for each question Title - The question's title Body - The body of the question Tags - The tags associated with the question in a space-seperated format (all lowercase, should not contain tabs '\t' or ampersands '&')
This is a multi-label classification problem (http://scikit-learn.org/stable/modules/multiclass.html)
Micro-Averaged F1-Score (Mean F Score) : The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal.
In the multi-class and multi-label case, this is the weighted average of the F1 score of each class.