skpro

library2.12.0pypypiunverified

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.

ai-ml data

pip install skpro

github ↗package ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · SKPRO

skpro

ai-mlpythonv2.12.0

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ProbabilisticRegressor

✓ from skpro.regression import ProbabilisticRegressor

✗ from skpro.regression import ProbabilisticRegressor

Fit a probabilistic regressor and evaluate using continuous ranked probability score (CRPS).

from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from skpro.regression import ProbabilisticRegressor from skpro.metrics import CRPS X, y = make_regression(n_samples=100, n_features=4, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) reg = ProbabilisticRegressor() reg.fit(X_train, y_train) # Predict distribution y_pred_dist = reg.predict_dist(X_test) # Evaluate with CRPS crps = CRPS() score = crps(y_test, y_pred_dist) print(f"CRPS: {score}")

Debug

Known issues

breakingPython 3.8 dropped in v2.8.0, Python 3.9 dropped in v2.10.0. Upgrade Python if using older versions.

fix

Use Python >=3.10.

affects: >=2.8.0

deprecatedDirect import of distribution classes from skpro.distributions submodules (e.g., skpro.distributions.normal) is deprecated; import from skpro.distributions instead.

fix

Use from skpro.distributions import NormalDistribution.

affects: >=2.9.0

gotchaPredict method returns a distribution object, not point predictions. Use .predict_mean() or .predict_interval() for point/interval estimates.

fix

Call reg.predict_mean(X_test) for point predictions, or reg.predict_interval(X_test) for intervals.

affects: all

Upgrade

Version history

2.12.0latest on PyPI · released Mar 14, 2026

Audit

Dependencies

sktimeoptionalOptional: used for time series integration, provides base estimators compatible with skpro.

scikit-learnrequiredRequired: used for base regression models and utilities.

numpyrequiredRequired: numerical operations and array handling.

Agent activity

13 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/sktime/skpro ↗

packagehttps://pypi.org/project/skpro/ ↗

docshttps://github.com/sktime/skpro ↗

changeloghttps://github.com/sktime/skpro ↗