{"library":"pytabkit","title":"PyTabKit","description":"PyTabKit provides ML models and a benchmark for tabular data classification and regression. Current version is 1.7.3, supporting Python >=3.9, with PyTorch-based models like RealMLP, xRFM, TabM, and TabICL wrappers, plus scikit-learn-style interfaces, HPO, and ensembling. Release cadence is irregular, with several updates in 2024-2025.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install pytabkit","pip install pytabkit[extra]"],"cli":null},"imports":["from pytabkit.models.sklearn.realmlp import RealMLP_Tabular_Classifier","from pytabkit.models.sklearn.realmlp import RealMLP_Tabular_Regressor","from pytabkit.models.sklearn.tabm import TabM_Classifier"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from pytabkit.models.sklearn.realmlp import RealMLP_Tabular_Classifier\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n\nX, y = make_classification(n_samples=100, n_features=20, random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\nmodel = RealMLP_Tabular_Classifier(\n    time_limit_s=30,  # limit training time\n    n_refit=1,\n    device='cpu'\n)\nmodel.fit(X_train, y_train)\ny_pred = model.predict(X_test)\nprint(f'Accuracy: {sum(y_pred == y_test) / len(y_test):.3f}')","lang":"python","description":"Quick example: train a RealMLP classifier on synthetic data with a time limit.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}