giuseppec / featureImportanceLinks
An R package to assess feature importance
☆33Updated 4 years ago
Alternatives and similar repositories for featureImportance
Users that are interested in featureImportance are comparing it to the libraries listed below
Sorting:
- R package EloML: Elo rating system for machine learning models☆24Updated 3 years ago
- Documentation for the DALEX project☆36Updated last year
- The R package M4comp2018 contains the 100000 time series from the M4-competition (https://www.m4.unic.ac.cy/)☆47Updated 6 years ago
- Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)☆83Updated last year
- Easy Hyper Parameter Optimization with mlr and mlrMBO.☆31Updated 3 years ago
- stray {Search and TRace AnomalY}. Full paper is available from https://arxiv.org/pdf/1908.04000.pdf☆59Updated last year
- Model verification, validation, and error analysis☆59Updated last year
- An R wrapper of SHAP python library☆58Updated 2 years ago
- ☆34Updated 3 years ago
- an R package for deriving Prediction Rule Ensembles☆58Updated 4 months ago
- GluonTS Deep Learning with Modeltime☆42Updated last year
- Time Series Ensemble Forecasting☆80Updated last month
- Helpers for parameters in black-box optimization, tuning and machine learning.☆26Updated 9 months ago
- Time Series Forecasting Using KNN☆11Updated last year
- Structure mining for xgboost model☆26Updated 4 years ago
- Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).☆20Updated last month
- Extensions for the DALEX package☆68Updated last year
- Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.☆44Updated last year
- Effects and Importances of Model Ingredients☆37Updated 2 years ago
- CRAN Task View: Anomaly Detection with R 🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛍️🛒🛒☆109Updated last week
- Parallelizable Bayesian Optimization in R☆113Updated 2 years ago
- The set of functions used for time series analysis and in forecasting.☆94Updated this week
- Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition