csinva / imodelsLinks
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
β1,505Updated last month
Alternatives and similar repositories for imodels
Users that are interested in imodels are comparing it to the libraries listed below
Sorting:
- Generate Diverse Counterfactual Explanations for any machine learning model.β1,454Updated 3 months ago
- A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.β1,475Updated this week
- Extra blocks for scikit-learn pipelines.β1,365Updated this week
- Fast SHAP value computation for interpreting tree-based modelsβ544Updated 2 years ago
- A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.β635Updated last year
- moDel Agnostic Language for Exploration and eXplanationβ1,442Updated this week
- Feature engineering package with sklearn like functionalityβ2,143Updated last month
- Predictive Power Score (PPS) in Pythonβ1,165Updated 3 weeks ago
- Algorithms for explaining machine learning modelsβ2,570Updated this week
- A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.β579Updated last year
- A Python package for modular causal inference analysis and model evaluationsβ795Updated 6 months ago
- Machine learning with dataframesβ1,475Updated last week
- Leave One Feature Out Importanceβ844Updated 8 months ago
- python partial dependence plot toolboxβ861Updated last year
- A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.β2,006Updated 4 months ago
- A Python library that helps data scientists to infer causation rather than observing correlation.β2,380Updated last year
- mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.β614Updated 11 months ago
- An extension of XGBoost to probabilistic modellingβ657Updated 2 months ago
- Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadβ¦β671Updated 8 months ago
- A drop-in replacement for Scikit-Learnβs GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.β469Updated last year
- Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.β2,451Updated 2 months ago
- A Python package for causal inference in quasi-experimental settingsβ1,046Updated last week
- The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to sβ¦β703Updated this week
- OmniXAI: A Library for eXplainable AIβ951Updated last year
- EvalML is an AutoML library written in python.β831Updated last week
- machine learning with logical rules in Pythonβ647Updated last year
- Multiple Imputation with LightGBM in Pythonβ391Updated last year
- A library for debugging/inspecting machine learning classifiers and explaining their predictionsβ312Updated 6 months ago
- Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Infβ¦β548Updated this week
- π Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Modelsβ2,963Updated 2 weeks ago