TabPFN’s internal preprocessing pipeline is one of the most powerful tuning levers. Each estimator in the ensemble cycles through a list of preprocessing configurations, creating diversity.
Control how features are transformed before being fed to the transformer.
Configuration Options
| Field | Default | Options |
|---|
name | (required) | "quantile_uni", "squashing_scaler_default", "safepower", "quantile_uni_coarse", "kdi", "robust", "none" |
categorical_name | "none" | "none", "numeric", "onehot", "ordinal", "ordinal_shuffled", "ordinal_very_common_categories_shuffled" |
append_original | False | True, False, "auto" |
max_features_per_estimator | 500 | int — subsamples features if above this limit |
global_transformer_name | None | None, "svd", "svd_quarter_components" |
For optimal diversity, use as many different preprocessing transforms as you have estimators (default 8). Each estimator cycles through the list.
y is transformed. This is especially useful for skewed targets:
from tabpfn import TabPFNRegressor
model = TabPFNRegressor(
inference_config={
"REGRESSION_Y_PREPROCESS_TRANSFORMS": (
"none",
"safepower",
"quantile_norm",
"quantile_uni",
"1_plus_log"
),
},
)
| Transform | When to Use |
|---|
"none" | Symmetric, well-behaved targets |
"safepower" | Skewed targets (handles negatives) |
"quantile_norm" | Heavily skewed or multi-modal targets |
"quantile_uni" | Alternative to quantile_norm |
"1_plus_log" | Non-negative targets with large range |
Other Inference Settings
model = TabPFNClassifier(
inference_config={
"POLYNOMIAL_FEATURES": "no", # "no", int, or "all" for O(n^2) interactions
"FINGERPRINT_FEATURE": True, # hash-based row identifier
"OUTLIER_REMOVAL_STD": "auto", # "auto" (12.0), None, or float
"SUBSAMPLE_SAMPLES": None, # None, int, float, or list
},
)
POLYNOMIAL_FEATURES: Generates interaction features. Can help when interactions matter but increases feature count quadratically.FINGERPRINT_FEATURE: Adds a hash-based row identifier. Useful by default; try disabling if you have very few features.OUTLIER_REMOVAL_STD: Removes extreme outliers before fitting. Lower values are more aggressive.SUBSAMPLE_SAMPLES: Subsample training rows for faster iteration during experimentation.
