facebookresearch / balanceLinks
The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
☆712Updated this week
Alternatives and similar repositories for balance
Users that are interested in balance are comparing it to the libraries listed below
Sorting:
- just a bunch of useful embeddings for scikit-learn pipelines☆518Updated last month
- skops is a Python library helping you share your scikit-learn based models and put them in production☆500Updated 2 weeks ago
- Fast SHAP value computation for interpreting tree-based models☆545Updated 2 years ago
- Doubt your data, find bad labels.☆516Updated last year
- Natural Intelligence is still a pretty good idea.☆823Updated last year
- ☆201Updated 3 weeks ago
- Streamline scikit-learn model comparison.☆143Updated 2 years ago
- Machine learning with dataframes☆1,491Updated this week
- Neo: Hierarchical Confusion Matrix Visualization (CHI 2022)☆314Updated last week
- A library for debugging/inspecting machine learning classifiers and explaining their predictions☆313Updated 6 months ago
- Compilation of high-profile real-world examples of failed machine learning projects☆744Updated last year
- Python package for conformal prediction☆539Updated last month
- A Simple Bulk Labelling Tool☆598Updated 3 months ago
- Gain clues from clustering!☆318Updated last year
- Extra blocks for scikit-learn pipelines.☆1,367Updated last week
- A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.☆1,486Updated this week
- 🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞☆719Updated 2 years ago
- WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)☆159Updated 3 years ago
- Coarse-grained lineage and tracing for machine learning pipelines.☆468Updated 3 years ago
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).