{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"NGBoost implements Natural Gradient Boosting for probabilistic prediction, built on scikit-learn, enabling regression models that output full probability distributions rather than point estimates.","skillfed_tags":["probabilistic-ml","uncertainty-quantification","gradient-boosting"],"use_cases":["Build regression models that output prediction intervals and uncertainty estimates alongside point forecasts.","Quantify model confidence in high-stakes domains like medical or financial prediction where uncertainty matters.","Compare different probability distributions and scoring rules for the same dataset to find the best fit.","Extend NGBoost with custom distributions or scoring rules for domain-specific probabilistic prediction tasks."],"what_it_does":"NGBoost is a gradient boosting library that outputs full probability distributions for each prediction rather than single point estimates. Built on scikit-learn, it implements the Natural Gradient Boosting algorithm, allowing you to quantify prediction uncertainty and choose among different distributions and scoring rules to match your problem. The library depends on numpy, scipy, scikit-learn, matplotlib, tqdm, lifelines, and sympy.\n\nYou use it like a standard estimator: fit on training data, then call predict() for point estimates or pred_dist() to get the full predictive distribution at each test point. This makes it useful when you need not just a prediction but also a measure of confidence or uncertainty around that prediction, such as in risk-sensitive applications or when building prediction intervals.","worth_installing":"Yes. NGBoost is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and fills a clear niche: probabilistic prediction with uncertainty quantification. Install it if you need prediction intervals, distribution outputs, or uncertainty estimates alongside your regression predictions."},"id":"ngboost","links":{"html":"https://skillfed.io/packages/ngboost","md":"https://skillfed.io/packages/ngboost.md","pypi":"https://pypi.org/project/ngboost/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-26","license_spdx":null,"license_treatment":"permissive","name":"ngboost","python_support":"supports_current","summary":"Library for probabilistic predictions via gradient boosting."},"popularity":{"monthly_downloads":179315,"position":10175,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.11"}