The Inductive Bias of Trees
Gradient Boosted Decision Trees (GBDTs) like XGBoost, LightGBM, and CatBoost possess an inductive bias perfectly suited for tabular data. They inherently handle irregular decision boundaries and don't require the extensive preprocessing (scaling, normalization) that neural networks demand.
When to use Deep Learning (TabNet, FT-Transformer)
Deep learning on tabular data (e.g., TabNet) begins to show value primarily in two scenarios:
- Massive Datasets: When data scales beyond memory limits and batch training becomes necessary.
- Multi-modal integration: When tabular data must be jointly embedded with images or text.
For standard classification/regression tasks on datasets < 10GB, start with trees.
Common Mistakes
1. Reaching for a neural net because 'it's modern'.
2. Failing to tune hyper-parameters of tree models before concluding they perform poorly.
3. One-hot encoding high cardinality categorical variables instead of using CatBoost's native handling.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape