> ## Documentation Index > Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # TabPFN Quickstart > Classification and regression with TabPFN.

By Eliott Kalfon

# TabPFN Quickstart Use TabPFN's pretrained model with scikit-learn's `fit` / `predict` workflow. Start with the hosted API; try local inference at the end. ![TabPFN pretraining on synthetic datasets and prediction on unseen data](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/quickstart/diagram.svg) ## Setup Install the client, local package, and example dependencies. ```python theme={null} %pip install -q tabpfn tabpfn-client scikit-learn ``` ## Authenticate Sign in at [Prior Labs](https://ux.priorlabs.ai), accept the license, and copy your API key into a Colab secret named `TABPFN_TOKEN`. Enable notebook access to that secret. ```python theme={null} import os from google.colab import userdata from tabpfn_client import set_access_token os.environ["TABPFN_TOKEN"] = userdata.get("TABPFN_TOKEN") set_access_token(userdata.get('TABPFN_TOKEN')) ``` ## Classification Split the German credit dataset, then evaluate held-out predictions with ROC AUC and accuracy. `fit` supplies context to the pretrained model without updating its weights. ```python theme={null} from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score, roc_auc_score from sklearn.model_selection import train_test_split from tabpfn_client import TabPFNClassifier X, y = fetch_openml(data_id=46562, as_frame=True, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42, stratify=y ) clf = TabPFNClassifier() clf.fit(X_train, y_train) prediction_probabilities = clf.predict_proba(X_test) print("ROC AUC:", roc_auc_score(y_test, prediction_probabilities[:, 1])) predictions = clf.predict(X_test) print("Accuracy", accuracy_score(y_test, predictions)) ``` ```console theme={null} ROC AUC: 0.8050636232454415 Accuracy 0.7818181818181819 ``` ## Regression Predict diabetes progression and report mean squared error, mean absolute error, and R². ```python theme={null} from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score from tabpfn_client import TabPFNRegressor 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, ) reg = TabPFNRegressor() reg.fit(X_train, y_train) predictions = reg.predict(X_test) print("Mean Squared Error (MSE):", mean_squared_error(y_test, predictions)) print("Mean Absolute Error (MAE):", mean_absolute_error(y_test, predictions)) print("R-squared (R^2):", r2_score(y_test, predictions)) ``` ```console theme={null} Mean Squared Error (MSE): 2698.666027142602 Mean Absolute Error (MAE): 40.93228039676196 R-squared (R^2): 0.5310957151096967 ``` ## Run locally Switch the import to `tabpfn` to run inference on your machine. The first use downloads model weights; a GPU is recommended for this example. ```python theme={null} from tabpfn import TabPFNClassifier X, y = fetch_openml(data_id=46562, as_frame=True, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42, stratify=y ) clf = TabPFNClassifier() # downloads weights on first use, then runs locally clf.fit(X_train, y_train) print("Local ROC AUC:", roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])) ``` ```console theme={null} Local ROC AUC: 0.8055446237264419 ```