// R packages for ML interpretability. Use for explaining and interpreting machine learning models.
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R่ฏญ่จ", "R่ๆฌ", "ggplot", "tidyverse", "ๆฐๆฎๅๆ", "ๅฏ่งๅ".
R DALEX package for model explanations. Use for explaining complex machine learning models.
R iml package for interpretable ML. Use for model-agnostic interpretability methods.
R lime package for local explanations. Use for explaining individual predictions with local interpretable models.
R vip package for variable importance. Use for computing and visualizing variable importance scores.
R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.
| name | r-ml-interpretability |
| description | R packages for ML interpretability. Use for explaining and interpreting machine learning models. |
Explain and interpret machine learning models.
| Package | Purpose |
|---|---|
| DALEX | Model explanations |
| iml | Interpretable ML |
| lime | Local explanations |
| vip | Variable importance |