TabPFN works well out of the box and handles many tasks natively that traditional ML pipelines require. We recommend feeding in data as raw as possible, as additional processing can hurt performance. Avoid additional scaling withDocumentation Index
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StandardScaler / MinMaxScaler, imputation of missing values, or one-hot encoding of categoricals.
Escalation Path
When the default TabPFN does not meet your needs, try these approaches in roughly this order:Feature engineering
Add domain features, extract datetime components, encode text meaningfully. This is usually the highest-impact change. See Feature Engineering.
Feature selection
If you have many features (100+), try filtering to the most informative ones. See Feature Selection.
Metric tuning
Use
eval_metric and tuning_config to optimize for your specific evaluation metric. See Model Parameters.Preprocessing transforms
Experiment with different
PREPROCESS_TRANSFORMS and target transforms. See Preprocessing Transforms.Fine-tuning
Fine-tune the pretrained model on your data when you have a specialized domain or distribution shift.
Guides
Feature Engineering
Encode domain knowledge into features that TabPFN cannot learn from raw columns alone.
Feature Selection
Reduce feature count to improve attention efficiency and predictive power.
Preprocessing Transforms
Configure TabPFN’s internal preprocessing pipeline for maximum ensemble diversity.
Model Parameters
Tune softmax temperature, metric optimization, and class imbalance handling.
Related
Fine-Tuning
Adapt TabPFN’s pretrained weights to your domain.