csinva / imodels
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
β1,441Updated 2 weeks ago
Alternatives and similar repositories for imodels:
Users that are interested in imodels are comparing it to the libraries listed below
- Generate Diverse Counterfactual Explanations for any machine learning model.β1,388Updated 3 months ago
- A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.β1,350Updated this week
- Extra blocks for scikit-learn pipelines.β1,312Updated this week
- moDel Agnostic Language for Exploration and eXplanationβ1,414Updated last month
- Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadβ¦β630Updated last month
- Algorithms for explaining machine learning modelsβ2,473Updated this week
- A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.β610Updated last year
- A Python package for causal inference in quasi-experimental settingsβ958Updated this week
- Feature engineering package with sklearn like functionalityβ2,012Updated this week
- A Python package for modular causal inference analysis and model evaluationsβ761Updated this week
- Fast SHAP value computation for interpreting tree-based modelsβ535Updated last year
- python partial dependence plot toolboxβ851Updated 6 months ago
- Prepping tables for machine learningβ1,322Updated this week
- A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.β574Updated 9 months ago
- Natural Gradient Boosting for Probabilistic Predictionβ1,688Updated 2 weeks ago
- Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.β632Updated 2 months ago
- Leave One Feature Out Importanceβ829Updated last month
- Multiple Imputation with LightGBM in Pythonβ373Updated 7 months ago
- A Library for Uncertainty Quantification.β905Updated this 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¦β696Updated last week
- Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Raβ¦β749Updated 7 months ago
- Multivariate exploratory data analysis in Python β PCA, CA, MCA, MFA, FAMD, GPAβ1,329Updated last week
- An extension of XGBoost to probabilistic modellingβ586Updated 8 months ago
- machine learning with logical rules in Pythonβ630Updated last year
- Python implementation of the rulefit algorithmβ417Updated last year
- PiML (Python Interpretable Machine Learning) toolbox for model development & diagnosticsβ1,244Updated 4 months ago
- π Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Modelsβ2,849Updated last month
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world dβ¦β426Updated last month
- A Python library that helps data scientists to infer causation rather than observing correlation.β2,288Updated 8 months ago
- mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.β569Updated 4 months ago