Editing Tabular Dl (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Tabular data''' — Structured data organized in rows (samples) and columns (features); the dominant format in enterprise ML.
* '''Heterogeneous features''' — Tabular data typically mixes numerical and categorical features of varying scales and semantics; unique challenge vs. images/text.
* '''Feature interactions''' — Relationships between features that jointly predict the target; gradient boosting discovers these via trees; DL via attention.
* '''Entity embedding''' — Representing categorical variables as learned dense vectors; a key technique enabling neural networks to handle high-cardinality categoricals.
* '''TabNet''' — An attention-based neural network for tabular data with built-in feature selection; Arik & Pfister (2021).
* '''TabTransformer''' — A transformer applying self-attention to categorical embeddings; Sheikh et al. (2021).
* '''FT-Transformer (Feature Tokenizer + Transformer)''' — Embeds all features (numerical + categorical) as tokens; applies transformer; Gorishniy et al. (2021).
* '''TabPFN''' — A pre-trained transformer that performs in-context learning on small tabular datasets; prior-fitted networks.
* '''SAINT''' — Self-Attention and Intersample Attention Transformer; applies attention both within and across samples.
* '''XGBoost / LightGBM / CatBoost''' — The dominant gradient boosting frameworks; still the baseline to beat on most tabular benchmarks.
* '''Prior-Data Fitted Networks (PFN)''' — Models pre-trained on synthetic tabular datasets that can perform few-shot inference on new datasets.
* '''Hyperparameter sensitivity''' — Neural networks for tabular data require careful tuning; GBDTs are more robust to hyperparameter choices.
* '''Large Language Models for tables''' — Using LLMs for tabular tasks via serialization; surprisingly competitive on certain tasks.
* '''AutoML''' — Automated ML pipeline search including architecture selection; FLAML, AutoGluon, H2O AutoML.
</div>

<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">