القائمة
R DALEX package for model explanations. Use for explaining complex machine learning models.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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 "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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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)