[QST] Understanding the feature dependency from Example

❓ Questions & Help

While training the model we are taking multiple features which also includes 'item_id-list_seq', 'category-list_seq' as categorical features and 'product_recency_days_log_norm-list_seq', 'et_dayofweek_sin-list_seq' as continuous variables. As per my understanding of training task the model is learning all 4 features separately and while evaluating it predicts all the features and take their ground truth to provide NDGC and recall.

If that's the case just wanted to understand that are the continuous features 'product_recency_days_log_norm-list_seq', 'et_dayofweek_sin-list_seq' needed by the model because as per my understanding these features are impacting the final results. Predicting the recency and time of interaction can be a tough task.

Could you please explain the dependency of these features and correct me if I have misunderstood any of the point.
I have tried training model only on categorical features and the results are pretty good, the model is able to predict next item id and category at a very good NDGC and recall.

Thanks

No files, tests, or entry points are named. Start by tracing the training and evaluation behavior described in the question, then document whether the continuous features are required and how feature predictions and ground truth affect NDCG and recall.