// R DALEX package for model explanations. Use for explaining complex 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 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 packages for ML interpretability. Use for explaining and interpreting machine learning 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 | DALEX |
| description | R DALEX package for model explanations. Use for explaining complex machine learning models. |
Descriptive mAchine Learning EXplanations.
library(DALEX)
# Create explainer
explainer <- explain(
model = model,
data = train_data,
y = train_labels,
label = "My Model"
)
# Model performance
perf <- model_performance(explainer)
plot(perf)
# Compare models
perf1 <- model_performance(explainer1)
perf2 <- model_performance(explainer2)
plot(perf1, perf2)
# Permutation importance
vi <- model_parts(explainer)
plot(vi)
# With options
vi <- model_parts(explainer,
loss_function = loss_root_mean_square,
B = 10)
# Partial dependence plot
pdp <- model_profile(explainer, variables = "age")
plot(pdp)
# Multiple variables
pdp <- model_profile(explainer, variables = c("age", "income"))
plot(pdp)
# Grouped
pdp <- model_profile(explainer, variables = "age", groups = "gender")
plot(pdp)
# Break down single prediction
bd <- predict_parts(explainer, new_observation = new_data[1, ])
plot(bd)
# SHAP values
shap <- predict_parts(explainer, new_observation = new_data[1, ],
type = "shap")
plot(shap)
# What-if analysis
cp <- predict_profile(explainer, new_observation = new_data[1, ])
plot(cp)
# Multiple observations
cp <- predict_profile(explainer, new_observation = new_data[1:3, ])
plot(cp)
# Residual diagnostics
diag <- model_diagnostics(explainer)
plot(diag)
# Interactive dashboard
library(arenar)
arena <- create_arena(live = TRUE) %>%
push_model(explainer)
run_server(arena)
# Multiple explainers
explainer1 <- explain(model1, data, y, label = "Model 1")
explainer2 <- explain(model2, data, y, label = "Model 2")
# Compare
vi1 <- model_parts(explainer1)
vi2 <- model_parts(explainer2)
plot(vi1, vi2)