{"library":"skpro","title":"skpro - Probabilistic Regression & Distribution Framework","description":"A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in Python. Provides sktime-compatible interfaces for distribution estimation, survival analysis, and conformal prediction. Current version: 2.12.0. Release cadence: ~3-4 major/minor releases per year.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install skpro"],"cli":null},"imports":["from skpro.regression import ProbabilisticRegressor","from skpro.distributions import TweedieDistribution","from skpro.regression.gaussian import GaussianNaiveRegressor"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from sklearn.datasets import make_regression\nfrom sklearn.model_selection import train_test_split\nfrom skpro.regression import ProbabilisticRegressor\nfrom skpro.metrics import CRPS\n\nX, y = make_regression(n_samples=100, n_features=4, random_state=42)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\nreg = ProbabilisticRegressor()\nreg.fit(X_train, y_train)\n\n# Predict distribution\ny_pred_dist = reg.predict_dist(X_test)\n\n# Evaluate with CRPS\ncrps = CRPS()\nscore = crps(y_test, y_pred_dist)\nprint(f\"CRPS: {score}\")","lang":"python","description":"Fit a probabilistic regressor and evaluate using continuous ranked probability score (CRPS).","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}