REP
github.com/yandex/repREP provides an IPython-based machine learning environment for consistent, reproducible data-driven research.
Looking for something like LightGBM? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 23 are open source.
The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | REP | — | Not stated | Linux, Windows |
| 2 | nilearn | — | Yes | — |
| 3 | DVClive | — | Yes | — |
| 4 | PredictionBuilder | — | Yes | — |
| 5 | YOLOv8 | — | Yes | — |
| 6 | pyro | — | Yes | Linux |
| 7 | Omnigraph | — | Yes | — |
| 8 | pytorch tabular | — | Yes | — |
| 9 | modAL | — | Yes | — |
| 10 | 19 Questions | — | Yes | — |
REP provides an IPython-based machine learning environment for consistent, reproducible data-driven research.
nilearn provides Python tools for machine learning and neuroimaging analysis.
DVCLive logs and tracks machine learning metrics, parameters, and models with Git or DVC.
This Swift library builds machine learning predictions using linear regression.
Ultralytics supports detection, segmentation, image classification, pose estimation, and tracking across YOLO model versions.
Pyro brings probabilistic programming capabilities to Python projects using PyTorch.
Omnigraph provides a graph engine for lakehouse data with Git-style workflows.
pytorch tabular provides a unified framework for deep learning models on tabular data.
modAL provides a modular active learning framework for Python built on top of scikit-learn.
19 Questions assigns attributes to objects using machine learning and Bayesian inference, according to its project description.
oneDAL is a C++ and DPC++ library for accelerated machine learning routines on tabular data, governed by the UXL Foundation.
InterpretML fits interpretable models and explains results from black-box machine learning systems.
A Python package to assess and improve fairness of machine learning models.
Manifold provides visual debugging for machine learning models without depending on a specific model type.
DVC tracks data versions and machine learning experiments for collaborative development.
Python package which helps to debug machine learning classifiers and explain their predictions.
An open source Python library for drift detection in machine learning systems.
LIME provides explanations for predictions made by machine learning classifiers.
MCGrad calibrates machine learning predictions across the full dataset and across segments defined by input features.
PredictionBuilder builds machine learning predictions using linear regression.
Cleanlab provides a Python library for data quality and machine learning with messy real world data and labels.
Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
PySS3 supports interpretable text classification with SS3 and visualization tools for explainable AI.
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative