tabpfn_classifier_breast_cancer_quickstart_with_ensemble.py

python

This quickstart demonstrates how to initialize the TabPFN classifier, fit it on a

15d ago17 lines

automl/TabPFN

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tabpfn_classifier_breast_cancer_quickstart_with_ensemble.py
import torch
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

from tabpfn import TabPFNClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# N_ensemble_configurations=32 is the default setting and yields the best performance
classifier = TabPFNClassifier(device='cpu', N_ensemble_configurations=32)

classifier.fit(X_train, y_train)
y_eval, p_eval = classifier.predict(X_test, return_winning_probability=True)

print('Accuracy', accuracy_score(y_test, y_eval))