Deep Learning With Weighted Cross Entropy Loss On Imbalanced Tabular Data Using FastAI

The dataset comes from the context of ad conversions where the binary target variables 1 and 0 correspond to conversion success and failure. This proprietary dataset (no, I don't own the rights) has some particularly interesting attributes due to its dimensions, class imbalance and rather weak relationship between the features and the target variable. First, the dimensions of the data: this tabular dataset contains a fairly large number of records and categorical features that have a very high cardinality. Note: In FastAI, categorical features are represented using embeddings which can improve classification performance on high cardinality features. Second, the binary class labels are highly imbalanced since successful ad conversions are relatively rare.