R DALEX package for model explanations. Use for explaining complex machine learning models.
Scanned 6/4/2026
Install via CLI
openskills install LeoLin990405/r-analytics-skill---
name: DALEX
description: R DALEX package for model explanations. Use for explaining complex machine learning models.
---
# DALEX
Descriptive mAchine Learning EXplanations.
## Create Explainer
```r
library(DALEX)
# Create explainer
explainer <- explain(
model = model,
data = train_data,
y = train_labels,
label = "My Model"
)
```
## Model Performance
```r
# Model performance
perf <- model_performance(explainer)
plot(perf)
# Compare models
perf1 <- model_performance(explainer1)
perf2 <- model_performance(explainer2)
plot(perf1, perf2)
```
## Variable Importance
```r
# Permutation importance
vi <- model_parts(explainer)
plot(vi)
# With options
vi <- model_parts(explainer,
loss_function = loss_root_mean_square,
B = 10)
```
## Partial Dependence
```r
# 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)
```
## Individual Predictions
```r
# 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)
```
## Ceteris Paribus
```r
# 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)
```
## Model Diagnostics
```r
# Residual diagnostics
diag <- model_diagnostics(explainer)
plot(diag)
```
## Arena (Interactive)
```r
# Interactive dashboard
library(arenar)
arena <- create_arena(live = TRUE) %>%
push_model(explainer)
run_server(arena)
```
## Compare Models
```r
# 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)
```
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