Classical & Tree-Based Models
These models form the bedrock of tabular ML and are the most widely used in practice.
- Linear/Logistic Regression: Simple, fast, and highly interpretable, but often not the most accurate as they only learn linear relationships.
- Random Forests: An "ensemble" of many individual decision trees. Robust, harder to overfit, and quite accurate.
- Gradient Boosted Decision Trees (GBDTs): The state-of-the-art. Models like XGBoost, LightGBM, and CatBoost build trees sequentially, where each new tree corrects the errors of the previous ones. They are dominant because they:
- Handle mixed data types (numbers and categories) natively.
- Do not require feature scaling (e.g., normalization).
- Are highly accurate and computationally efficient.
Deep Learning for Tabular Data
These models use neural networks to learn complex, non-linear patterns. They have shown great success in vision and language but are less dominant in tabular data.
- Multi-Layer Perceptrons (MLPs): The simplest neural network. They require careful preprocessing, such as one-hot encoding for categories and scaling for numbers.
- Embeddings: The key innovation. Instead of one-hot encoding, categorical features (like 'product_id' or 'zip_code') are mapped to a low-dimensional vector space (an "embedding"). This allows the model to learn the "meaning" or similarity between different categories.
- Specialized Architectures: Models like TabNet and AutoInt are custom-built neural networks that use attention mechanisms (similar to Transformers) to learn which features are most important for a given prediction.
The Concept of "Large Tabular Models" (LTMs)
This is an emerging and ambitious research area, heavily inspired by the success of Large Language Models (LLMs) like GPT.
The Core Idea: Can we pre-train one single, massive "foundation model" on a huge and diverse collection of *all kinds* of tabular datasets (e.g., public data from finance, biology, retail, etc.)?
This pre-trained LTM would learn universal, general-purpose patterns of tabular data. Then, an enterprise could "fine-tune" this model on its own small, specific dataset (e.g., for churn prediction) and achieve high accuracy with very little data and effort.
Major Challenges:
- Heterogeneity: Tabular data is chaotic. Unlike text, columns have no inherent order, and schemas are different for every dataset. A column named 'age' in one table (a person) means something completely different than 'age' in another (a building).
- Data Access: It is extremely difficult to create a massive, diverse, and representative corpus of public tabular data for pre-training, unlike the web-scraped text used for LLMs.
Current Status: This is mostly a research concept. The dominant paradigm is still training a specific model (like a GBDT) on a specific dataset for a specific task.