bgreenwell / pdp
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
☆94Updated 2 years ago
Alternatives and similar repositories for pdp:
Users that are interested in pdp are comparing it to the libraries listed below
- An R wrapper of SHAP python library☆59Updated last year
- Bayesian comparisons of models using resampled statistics☆102Updated 6 months ago
- An R package for common supervised machine learning metrics.☆100Updated 5 years ago
- xray: The R Package to have X Ray vision on your datasets☆75Updated 7 years ago
- Making imputation easy☆91Updated 8 months ago
- Padding of missing records in time series☆133Updated 4 months ago
- An R package for interactive visualization of GAM models☆76Updated last week
- Automatic tuning of random forests☆33Updated last year
- A simple hello world R-docker example☆52Updated 5 years ago
- An interactive free online short course on the drake R package☆145Updated 4 years ago
- Use dplyr verbs to build data.table expressions☆66Updated 2 years ago
- Example workflows for the drake R package☆56Updated 3 years ago
- Tidy machine learning pipelines☆133Updated 7 years ago
- An R interface to the Python module Featuretools☆49Updated 4 years ago
- Extra recipes for predictor embeddings☆142Updated 2 months ago
- modelDown generates a website with HTML summaries for predictive models☆121Updated 2 years ago
- fable extension for the prophet forecasting procedure☆56Updated last year
- Simplifies plotting of database and sparklyr data☆124Updated 4 years ago
- Join tables based on events occurring in sequence in a funnel.☆140Updated 2 years ago
- Materials for getting to the know the R package purrr☆115Updated 5 years ago
- Convenience functions for working with merMod objects from lme4☆104Updated last year
- Time-aware tibbles☆178Updated 4 months ago
- exploratory data analysis using random forests☆69Updated 7 years ago
- Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current rel…☆74Updated 5 months ago
- Parsnip backends for `tree`, `lightGBM` and `Catboost`☆86Updated 2 years ago
- The most recent version of the Applied Machine Learning notes☆101Updated 2 years ago
- 💪 🤔 Modern Super Learning with Machine Learning Pipelines☆101Updated 5 months ago
- useR! 2019 Tutorial: Automatic and Explainable Machine Learning with H2O in R (http://www.user2019.fr/tutorials/)☆26Updated 5 years ago
- shiny-mlr: Integration of the mlr package into shiny☆93Updated 6 years ago
- data.frame-based API for model and predict functions☆60Updated 7 years ago