quantile-forest
quantile-forest: A focused Python library for conditional quantiles, ranks, proximity counts, and intervals. Ranked #28 of 38 in Predictive Analytics Software by our editors (6.6/10); pricing: Free plan; best for developers needing quantile predictions.
At a glance
- Editor score6.6 / 10
- PricingFree plan
- Best forDevelopers needing quantile predictions
- Free planYes
- Paid fromFree
- Forecasting workflowsNo
- Facts checked24 Sep 2026
Where it wins
- Estimates arbitrary conditional quantiles at prediction time without retraining
- Offers random forest and extra-trees quantile regression estimators
- Adds out-of-bag estimation, quantile ranks, and proximity counts
Where it doesn't
- It is a library, not a hosted analytics service
- Does not provide forecasting workflows
- Does not provide automated machine learning
Our verdict on quantile-forest
For developers working in Python who need conditional quantile estimates, quantile-forest provides quantile regression forest estimators with a scikit-learn-compatible API. Its random forest and extra-trees estimators are designed as drop-in replacements for scikit-learn forest regressors, so it fits projects already organized around that ecosystem. This is a focused machine-learning library rather than a complete predictive analytics platform: it is most relevant when quantile predictions are central to a model workflow, not when a team is looking for a hosted service or end-to-end analytics environment.
A defining capability is estimating arbitrary conditional quantiles at prediction time without retraining. That can support prediction intervals using quantile estimates, while quantile ranks and proximity counts add ways to characterize observations. Out-of-bag estimation is also included. Together, these features make the package more specific than a general forest-regression wrapper, but they remain developer-facing methods that need to be incorporated into an application or analysis workflow. Teams seeking automated model-building or a forecasting workflow should look elsewhere; those implementing quantile-based methods in Python may find the narrower scope appropriate.
The package is open source and self-hosted, with API access and documentation as its support channel. The listed pricing model is open source, with a free plan, so there are no tier-by-tier paid features to compare here. Scikit-learn compatibility is its main ecosystem fit; the published integration list names scikit-learn. Because it is not a hosted analytics service, buyers should expect to work with the package in their own environment rather than select a vendor-operated deployment. Choose quantile-forest when control over a Python modeling workflow and quantile estimates matter most. Prefer a broader predictive analytics product if you need hosted operations, automated machine learning, or forecasting workflows in one service.
Your product? Our editors can test quantile-forest in depth and publish a review, labelled as paid · from $149/mo →
quantile-forest pricing
quantile-forest fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Forecasting workflows | No |
| Model evaluation | Not verified |
| Automated machine learning | No |
| Deployment mode | Not verified |
| Time-series modeling | Not verified |
| Data connectors | Not verified |
| API access | Yes |
| Deployment | Self-hosted |
| Support | Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 1 integrations: scikit-learn |
| Pricing | Free plan |
| Website | zillow.github.io |
| Facts checked | 24 Sep 2026 |
quantile-forest integrations
quantile-forest lists 1 integrations on its own site.
- scikit-learn
Alternatives to quantile-forest
- SAS ViyaA broad, governed analytics platform for operational predictive decisioning.9.4
- SAS Advanced AnalyticsA broad enterprise platform for forecasting, machine learning, and governed model deployment.9.1
- Azure Machine LearningA broad managed ML lifecycle service, particularly suited to teams already working in Azure.9.0
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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