incerto.dev

What It Is

incerto is a Python library for uncertainty quantification in machine learning, with the current text showing version v0.1.0. Its goal is not to train general-purpose models, but to provide tools around the question of whether a model “knows when it is uncertain.” This includes calibration, out-of-distribution detection, conformal prediction, selective prediction, Bayesian deep learning, active learning, distribution drift detection, and LLM uncertainty analysis.

Core Capabilities and Ecosystem

In terms of feature coverage, incerto has a fairly comprehensive module structure. Calibration supports methods such as Temperature Scaling, Platt, Isotonic, Dirichlet, and Beta, and provides metrics including ECE, MCE, Brier, and NLL. OOD detection covers MSP, Energy, ODIN, Mahalanobis, KNN, and more. Conformal prediction includes APS, RAPS, Jackknife+, and CV+. Selective prediction supports confidence thresholds, SAT, Deep Gambler, and SelectiveNet. The LLM component includes token entropy, perplexity, self-consistency, semantic entropy, and related methods. Its technical stack is clearly oriented toward Python/PyTorch, requiring Python 3.10+ and PyTorch 2.0+, with optional extensions via torchvision, transformers, and sentence-transformers for vision and LLM use cases.

Installation, Pricing, and Documentation

Installation is straightforward: you can run pip install incerto, or clone the source code from GitHub and install it manually. Optional dependencies are available for vision, llm, and all. The text does not mention any paid plans, and since GitHub and source installation are provided, it can be considered a free open-source library, although license information was not present in the captured content. The documentation appears relatively strong, including a Quick Start, method selection guide, multiple topic-specific guides, API Reference, and sample code, making it friendly for research and prototyping.

Pros and Cons

Its main advantage is broad coverage: it brings many common model reliability tasks into a unified API. The examples are close to real-world workflows, such as calibration validation sets, OOD AUROC, production drift monitoring, and LLM semantic entropy. The downside is that the version is only v0.1.0, so its maturity, compatibility, and long-term maintenance remain to be seen. The text does not specify the license, maintenance team, community size, SLA, or enterprise support, so it should be thoroughly evaluated before being used in production-critical systems.

Who It’s For and Access from China

It is suitable for PyTorch researchers, ML platform teams, AI safety and reliability engineers, and application teams that need to assess uncertainty in LLM outputs. For access from China, the captured text is insufficient to determine the availability of incerto.dev, GitHub, or PyPI. In practice, usage may be affected by network conditions when accessing GitHub, PyPI, or downloading Hugging Face models. Alternatives to consider include in-house PyTorch implementations, TensorFlow Probability, Pyro, MAPIE, Evidently AI, and Alibi Detect.