Outperform traditional machine learning methods with dramatically faster inference. A transformer-based foundation model designed for supervised classification and regression on tabular data.
Traditional ML methods require hours or days to train on large datasets.
Conventional models hit performance ceilings on complex tabular data.
Requires extensive feature engineering and hyperparameter tuning.
Achieve state-of-the-art performance in seconds, not hours.
Outperforms XGBoost, Random Forest, and other traditional methods.
Minimal preprocessing required - just load and predict.
Built for modern tabular machine learning with cutting-edge transformer architecture
Advanced transformer-based design that captures complex patterns in tabular data.
Handles up to 50,000 rows and 2,000 features with ease.
Achieve state-of-the-art results in seconds, not hours.
Supports both classification and regression tasks seamlessly.
Consistently outperforms traditional methods across diverse datasets.
Simple pip installation and scikit-learn compatible API.
TabPFN consistently outperforms traditional methods across multiple datasets
Get started with TabPFN in minutes with our simple installation process
pip install tabpfn
pip install "tabpfn @ git+https://github.com/PriorLabs/TabPFN.git"
Requires uv (version 0.10.0 or higher)
git clone https://github.com/PriorLabs/TabPFN.git --depth 1
cd TabPFN
uv sync
Start using TabPFN with just a few lines of code
# Import TabPFN
from tabpfn import TabPFNClassifier
import numpy as np
# Create TabPFN classifier (default TabPFN-2.6)
classifier = TabPFNClassifier()
# Prepare your data
X_train = np.random.rand(1000, 20) # 1000 samples, 20 features
y_train = np.random.randint(0, 2, 1000) # Binary labels
X_test = np.random.rand(100, 20)
# Train and predict (in seconds!)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
# Get prediction probabilities
probabilities = classifier.predict_proba(X_test)
print(probabilities)
TabPFN excels in various real-world applications
Credit scoring, fraud detection, and risk analysis
Medical diagnosis, patient outcome prediction
Customer segmentation, recommendation systems
Quality control, predictive maintenance
Employee retention, performance prediction
Rapid prototyping, baseline model comparison
Join thousands of data scientists who are already using TabPFN to achieve state-of-the-art results in record time.