aplr
Automatic Piecewise Linear Regression
Decision gist · record as of 2026-08-14
Yes, if you need interpretable regression or classification models and want an alternative to tree-based or black-box methods. The active maintenance, permissive MIT license, and lack of known vulnerabilities make it a low-risk choice. Medium install friction is typical for compiled packages and should not be a barrier. Start with the published article and examples to assess whether the methodology fits your problem.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; numpy and pandas must be installed as runtime dependencies.
- Medium install friction due to compiled wheels across multiple platforms (Windows, macOS, Linux).
- Active maintenance with a release 1 day old and last commit on 2026-08-13 suggests responsive development.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute APLR with minimal restrictions, provided you include the license notice.
last release 2026-08-13 (1 days) · last repo commit 2026-08-13 · 25 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 334,301 downloads/mo, #7,493 on PyPI
Alternatives
Verify before relying
pip install aplr
import pandas as pd
from aplr import APLR
model = APLR()
model.fit(X_train, y_train)
predictions = model.predict(X_test)- Whether APLR's accuracy claims against tree-based methods hold across diverse datasets and problem types.
- Performance characteristics and scalability limits for large datasets.
- Whether the optional plotting dependencies (aplr[plots]) are necessary for typical workflows.
What it is and what it does
APLR is a machine learning library that implements automatic piecewise linear regression, a methodology for building predictive models that split the input space into regions and fit linear models within each. It targets both regression and classification tasks, working with numpy arrays and pandas DataFrames. The core appeal is interpretability: piecewise linear models are easier to understand and explain than black-box methods like neural networks or complex tree ensembles, while the library claims to match or exceed their predictive accuracy in many cases.
The package provides a scikit-learn-like API with fit and predict methods, plus a tuning utility for hyperparameter optimization. It requires numpy and pandas as runtime dependencies and supports Python 3.8 and later across Windows, macOS, and Linux. The library is actively maintained, with recent releases and responsive development.
Use it for
- Building regression models where interpretability is as important as accuracy, such as in finance or healthcare.
- Classification tasks where you need to explain decision boundaries to stakeholders or regulators.
- Benchmarking against tree-based methods when you want smoother, more continuous predictions.
- Tuning hyperparameters systematically using the provided APLR tuner utility.
- Exploratory data analysis where piecewise linear fits reveal structure in relationships between variables.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need interpretable regression or classification models and want an alternative to tree-based or black-box methods.
The active maintenance, permissive MIT license, and lack of known vulnerabilities make it a low-risk choice. Medium install friction is typical for compiled packages and should not be a barrier. Start with the published article and examples to assess whether the methodology fits your problem.
Install
aplr on PyPI
Before you install
Medium install friction due to compiled wheels across multiple platforms (Windows, macOS, Linux). Active maintenance with a release 1 day old and last commit on 2026-08-13 suggests responsive development. Depends on numpy and pandas, both widely available.
Requires Python 3.8 or later; numpy and pandas must be installed as runtime dependencies.
License in practice
MIT license is permissive; you can use, modify, and distribute APLR with minimal restrictions, provided you include the license notice.
Quickstart
pip install aplr
import pandas as pd
from aplr import APLR
model = APLR()
model.fit(X_train, y_train)
predictions = model.predict(X_test)Verify before relying
- Whether APLR's accuracy claims against tree-based methods hold across diverse datasets and problem types.
- Performance characteristics and scalability limits for large datasets.
- Whether the optional plotting dependencies (aplr[plots]) are necessary for typical workflows.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpypandas |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 334,301 / month, #7,493 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT License |
Evidence: aplr-10.24.1-cp310-cp310-macosx_11_0_arm64.whl; aplr-10.24.1-cp310-cp310-macosx_11_0_x86_64.whl; aplr-10.24.1-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; aplr-10.24.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; aplr-10.24.1-cp310-cp310-win32.whl; aplr-10.24.1-cp310-cp310-win_amd64.whl; aplr-10.24.1-cp310-cp310-win_arm64.whl; aplr-10.24.1-cp311-cp311-macosx_11_0_arm64.whl; aplr-10.24.1-cp311-cp311-macosx_11_0_x86_64.whl; aplr-10.24.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; aplr-10.24.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; aplr-10.24.1-cp311-cp311-win32.whl; aplr-10.24.1-cp311-cp311-win_amd64.whl; aplr-10.24.1-cp311-cp311-win_arm64.whl; aplr-10.24.1-cp312-cp312-macosx_11_0_arm64.whl; aplr-10.24.1-cp312-cp312-macosx_11_0_x86_64.whl; aplr-10.24.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; aplr-10.24.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; aplr-10.24.1-cp312-cp312-win32.whl; aplr-10.24.1-cp312-cp312-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “regression classification python”
- aplrAPLR builds interpretable regression and classification models using…
- sktimesktime provides a unified interface for time series machine learning…
- libcuml-cu12GPU-accelerated machine learning algorithms with…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also piecewise-regression · pwlf · ropwr · lime · interpret-core · interpret · pyjpt · skope-rules · linearmodels · glum