Regression

TabPFN can be applied to regression tasks using the TabPFNRegressor class. Instead of a single point estimate, the regressor predicts an output distribution, enabling you to generate different types of forecasts and accurately measure uncertainty in your predictions.

Getting Started

The following shows a full example of using the TabPFNRegressor:

from sklearn.datasets import load_diabetes
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNRegressor
from tabpfn.constants import ModelVersion

# Load example dataset
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Initialize and fit
model = TabPFNRegressor()
# To use TabPFNv2:
# model = TabPFNRegressor.create_default_for_version(ModelVersion.V2)
model.fit(X_train, y_train)

# Predict
preds = model.predict(X_test)

# Evaluate
print("MSE:", mean_squared_error(y_test, preds))
print("MAE:", mean_absolute_error(y_test, preds))
print("R²:", r2_score(y_test, preds))

Understanding Predictions

The TabPFNRegressor.predict() method can return different types of predictions based on the output_type parameter.

Point Predictions (Mean, Median, Mode)

By default, predict() returns the mean of the predicted distribution. You can also request the median or mode for different point estimates.

# Get the mean prediction (default)
mean_preds = model.predict(X_test, output_type="mean")

# Get the median prediction
median_preds = model.predict(X_test, output_type="median")

# Get the mode prediction
mode_preds = model.predict(X_test, output_type="mode")

Quantile Regression

To perform quantile regression and get predictions for specific quantiles, set output_type="quantiles" and pass a list of desired quantiles. This is ideal for estimating prediction intervals.

# Get predictions for the 10th, 50th (median), and 90th quantiles
quantile_preds = model.predict(
    X_test,
    output_type="quantiles",
    quantiles=[0.1, 0.5, 0.9],
)

# quantile_preds is a list of arrays
# q10_preds = quantile_preds[0]
# q50_preds = quantile_preds[1]
# q90_preds = quantile_preds[2]

Full Distribution

For advanced use cases, you can retrieve the full predictive distribution.

output_type="main" returns a dictionary containing the mean, median, mode, and default quantiles.
output_type="full" returns everything in "main" plus the raw logits and criterion object, allowing for custom loss calculations (see the FAQ).

# Get a dictionary of all main prediction types
main_outputs = model.predict(X_test, output_type="main")
# print(main_outputs.keys()) 
# > dict_keys(['mean', 'median', 'mode', 'quantiles'])

# Get the full distribution objects for custom analysis
full_outputs = model.predict(X_test, output_type="full")
# print(full_outputs.keys())
# > dict_keys(['mean', 'median', 'mode', 'quantiles', 'criterion', 'logits'])

How can I apply a custom loss (e.g. MAPE, asymmetric loss) to regression?

TabPFNRegressor provides a full predictive distribution, enabling loss-aware predictions without retraining. You can compute the Bayes-optimal point prediction that minimizes the expected custom loss. This method gives flexible custom-loss predictions without modifying the model.

out = reg.predict(X, output_type="full")
criterion, logits = out["criterion"], out["logits"]

q = np.linspace(0.01, 0.99, 101)
samples = np.stack(
    [criterion.icdf(logits, float(x)).cpu().numpy() for x in q], axis=1
)

denom = np.maximum(np.abs(samples), 1e-6)
diffs = np.abs(samples[:, :, None] - samples[:, None, :])
exp_mape = (diffs / denom[:, :, None]).mean(axis=1)
best_idx = exp_mape.argmin(axis=1)
y_hat = samples[np.arange(samples.shape[0]), best_idx]

Predictive Distribution

Deep dive into the bar distribution: visualize uncertainty, extract quantiles, and handle multimodal posteriors.

Interpretability

Explain TabPFN predictions with SHAP utilities.

Getting Started

API

Capabilities

Agentic

Integrations

Troubleshooting

Use Cases

Changelog

Getting Started

Understanding Predictions

Point Predictions (Mean, Median, Mode)

Quantile Regression

Full Distribution

Predictive Distribution

Interpretability

​Getting Started

​Understanding Predictions

​Point Predictions (Mean, Median, Mode)

​Quantile Regression

​Full Distribution

​Related

Predictive Distribution

Interpretability

Getting Started

Understanding Predictions

Point Predictions (Mean, Median, Mode)

Quantile Regression

Full Distribution

Related