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¶
from tabpfn.scripts.estimator.post_hoc_ensembles import AutoTabPFNRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
import numpy as np
import sklearn
reg = AutoTabPFNRegressor(device='auto’, max_time=30)
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))