Regression¶
TabPFN can also be applied to regression tasks using the TabPFNRegressor class. This allows for predictive modeling of continuous outcomes.
Example¶
An example usage of TabPFNRegressor is shown below:
from tabpfn_client import TabPFNRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
import numpy as np
import sklearn
reg = TabPFNRegressor(device='auto')
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.fit(X_train, y_train)
preds = reg.predict(X_test)
print('Mean Squared Error (MSE): ', sklearn.metrics.mean_squared_error(y_test, preds))
print('Mean Absolute Error (MAE): ', sklearn.metrics.mean_absolute_error(y_test, preds))
print('R-squared (R^2): ', sklearn.metrics.r2_score(y_test, preds))
from tabpfn import TabPFNRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
import numpy as np
import sklearn
reg = TabPFNRegressor(device='auto')
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.fit(X_train, y_train)
preds = reg.predict(X_test)
print('Mean Squared Error (MSE): ', sklearn.metrics.mean_squared_error(y_test, preds))
print('Mean Absolute Error (MAE): ', sklearn.metrics.mean_absolute_error(y_test, preds))
print('R-squared (R^2): ', sklearn.metrics.r2_score(y_test, preds))
This example demonstrates how to train and evaluate a regression model. For more details on TabPFNRegressor and its parameters, refer to the API Reference section.
Example with AutoTabPFNRegressor¶
Abstract
AutoTabPFNRegressor yields the most accurate predictions for TabPFN and is recommended for most use cases. The AutoTabPFNClassifier and AutoTabPFNRegressor automatically run a hyperparameter search and build an ensemble of strong hyperparameters. You can control the runtime using ´max_time´ and need to make no further adjustments to get best results.
from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
import numpy as np
import sklearn
reg = AutoTabPFNRegressor(max_time=30) # runs for 30 seconds
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.fit(X_train, y_train)
preds = reg.predict(X_test)
print('Mean Squared Error (MSE): ', sklearn.metrics.mean_squared_error(y_test, preds))
print('Mean Absolute Error (MAE): ', sklearn.metrics.mean_absolute_error(y_test, preds))
print('R-squared (R^2): ', sklearn.metrics.r2_score(y_test, preds))