Fraunhofer-SCAI / corr_shapLinks
☆33Updated last year
Alternatives and similar repositories for corr_shap
Users that are interested in corr_shap are comparing it to the libraries listed below
Sorting:
- Python package for missing-data imputation with deep learning☆155Updated last year
- Fast implementation of Venn-ABERS probabilistic predictors☆75Updated last year
- Quantile Regression Forests compatible with scikit-learn.☆242Updated 2 weeks ago
- Probabilistic prediction with XGBoost.☆118Updated 6 months ago
- A regression solver for high dimensional penalized linear, quantile and logistic regression models'☆91Updated 3 months ago
- ☆20Updated last week
- ☆120Updated this week
- Python implementation of the conformal prediction framework.☆471Updated 4 years ago
- Bayesian time series forecasting and decision analysis☆117Updated 2 years ago
- Tools for conformal inference in regression☆248Updated last year
- Random Forests for Conditional Density Estimation☆42Updated 4 years ago
- Distance correlation and related E-statistics in Python☆155Updated last year
- tsbootstrap: generate bootstrapped time series samples in Python☆82Updated 3 months ago
- An extension of CatBoost to probabilistic modelling☆147Updated last year
- Scikit-learn compatible decision trees beyond those offered in scikit-learn☆85Updated last week
- Feature selection in neural networks☆243Updated last year
- A power-full Shapley feature selection method.☆210Updated this week
- Functional Data Analysis Python package☆334Updated 3 months ago
- Nonlinear Nonparametric Statistics☆92Updated this week
- All Relevant Feature Selection☆141Updated 6 months ago
- Generating and Imputing Tabular Data via Diffusion and Flow XGBoost Models☆166Updated last year
- R package for Adaptive Conformal Inference☆17Updated last year
- An extension of XGBoost to probabilistic modelling☆648Updated 2 months ago
- Python implementation of adversarial random forests for density estimation and generative modelling☆31Updated last year
- ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any…☆102Updated 3 years ago
- An extension of LightGBM to probabilistic modelling☆334Updated 2 months ago
- A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and …☆63Updated 3 years ago
- For calculating global feature importance using Shapley values.☆276Updated last week
- Feature selection package based on SHAP and target permutation, for pandas and Spark☆31Updated 3 years ago
- Python package for conformal prediction☆521Updated this week