![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F83267%2F45b4dd7c2919349e7ea08aef42d74014%2Finput_length_in_test.png?generation=1667714619896082&alt=media\")
Hey!
\nVery often during modeling we might want to throw out data that is not convenient to work with.
\nAn excellent example of this is shortening sentences for NLP. We might have a lot of sentences with some word count, and then just a handful of sentences that are extremely long. If we are training a language model, we might be tempted to discard longer sequences.
\nIn this competition, we have a similar scenario. I emulated how a test set is likely to have been constructed. You can find the methodology in the 💡A robust local validation framework 🚀🚀🚀 notebook.
\nHere is how input lengths might break down in test (with the column on the right being percentage of all input sequences).
\n
We can see that the vast majority of sessions are short. We might want to discard the longer sequences.
\nBut that would be a grave mistake!
\nThis is again based on the input lengths we are likely to see in test. See how disproportionately longer sessions result in orders, the highest-valued predictions.
\nFrom the competition description we know that this is how results are weighted: {'clicks': 0.10, 'carts': 0.30, 'orders': 0.60}
And now let's look at how carts are distributed by input length in the emulated test set
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F83267%2F053d4bbab9f6c5487a6eeb58bb16eebf%2Finput_length_in_test_carts.png?generation=1667714909209643&alt=media\")
And here is how the situation looks like for orders (the disproportion is even greater!)
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F83267%2Fbe073f33d0144199d0eb4011270fdca1%2Finput_length_in_test_orders.png?generation=1667714972605857&alt=media\")
This is not surprising in some sense. We might expect people who buy to spend more time on the website. Still, if we follow standard modeling practices and disregard those longer sequences, we would be severely limiting our potential in this competition!
\nThose longer sequences contribute to a disproportionate degree to the overall score!
\nHey!
\nVery often during modeling we might want to throw out data that is not convenient to work with.
\nAn excellent example of this is shortening sentences for NLP. We might have a lot of sentences with some word count, and then just a handful of sentences that are extremely long. If we are training a language model, we might be tempted to discard longer sequences.
\nIn this competition, we have a similar scenario. I emulated how a test set is likely to have been constructed. You can find the methodology in the 💡A robust local validation framework 🚀🚀🚀 notebook.
\nHere is how input lengths might break down in test (with the column on the right being percentage of all input sequences).
\n
We can see that the vast majority of sessions are short. We might want to discard the longer sequences.
\nBut that would be a grave mistake!
\nThis is again based on the input lengths we are likely to see in test. See how disproportionately longer sessions result in orders, the highest-valued predictions.
\nFrom the competition description we know that this is how results are weighted: {'clicks': 0.10, 'carts': 0.30, 'orders': 0.60}
And now let's look at how carts are distributed by input length in the emulated test set
And here is how the situation looks like for orders (the disproportion is even greater!)
This is not surprising in some sense. We might expect people who buy to spend more time on the website. Still, if we follow standard modeling practices and disregard those longer sequences, we would be severely limiting our potential in this competition!
\nThose longer sequences contribute to a disproportionate degree to the overall score!
\nThanks so much @radek1 for this wonderful post! This is very helpful!
\nHere is how I understand your post, could you help me verify?
\na question here, what do you consider to be a long session? \"25-10000\" is surely very very long, how about 10-25 and 5-10? do you consider them to be long?
\nA session with more events not only provide more info to generate possible predictions but also indicate the user is more likely to put products into cart and even order.
because of the point above and metric using weights which take carts and orders more seriously {'clicks': 0.10, 'carts': 0.30, 'orders': 0.60}, these longer sessions will help us score higher. Therefore we shouldn't discard but treasure longer sessions.
especially when longer sessions on carts and orders have proportions which is not tiny as you showed in your charts above
Thanks so much @radek1 for this wonderful post! This is very helpful!
\nHere is how I understand your post, could you help me verify?
\na question here, what do you consider to be a long session? \"25-10000\" is surely very very long, how about 10-25 and 5-10? do you consider them to be long?
\nA session with more events not only provide more info to generate possible predictions but also indicate the user is more likely to put products into cart and even order.
because of the point above and metric using weights which take carts and orders more seriously {'clicks': 0.10, 'carts': 0.30, 'orders': 0.60}, these longer sessions will help us score higher. Therefore we shouldn't discard but treasure longer sessions.
especially when longer sessions on carts and orders have proportions which is not tiny as you showed in your charts above