Menu
R vip package for variable importance. Use for computing and visualizing variable importance scores.
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".
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 packages for ML interpretability. Use for explaining and interpreting machine learning models.
R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.
| name | vip |
| description | R vip package for variable importance. Use for computing and visualizing variable importance scores. |
Variable Importance Plots.
library(vip)
# Variable importance plot
vip(model)
# With options
vip(model, num_features = 10)
# Model-specific (default)
vip(model, method = "model")
# Permutation-based
vip(model, method = "permute",
train = train_data,
target = "y",
metric = "rmse")
# SHAP-based
vip(model, method = "shap",
train = train_data)
# FIRM (feature importance ranking measure)
vip(model, method = "firm",
train = train_data)
# Permutation importance
vi_perm <- vi_permute(
model,
train = train_data,
target = "y",
metric = "rmse",
nsim = 10
)
# Plot
vip(vi_perm)
# SHAP-based importance
vi_shap <- vi_shap(
model,
train = train_data
)
vip(vi_shap)
# Custom loss function
my_metric <- function(actual, predicted) {
mean(abs(actual - predicted))
}
vi_permute(model, train = train_data, target = "y",
metric = my_metric)
# Partial dependence plots
library(pdp)
# Single variable
partial(model, pred.var = "age", train = train_data) %>%
autoplot()
# Two variables
partial(model, pred.var = c("age", "income"), train = train_data) %>%
autoplot()
# Get importance values
vi(model)
# As data frame
vi_model(model)
# Sorted
vi(model) %>%
arrange(desc(Importance))
vip(model,
num_features = 10,
geom = "point", # or "col", "boxplot"
aesthetics = list(
color = "steelblue",
fill = "steelblue"
))
# Horizontal
vip(model, horizontal = TRUE)
# Include zero
vip(model, include_type = TRUE)
# Compare models
vi1 <- vi(model1)
vi2 <- vi(model2)
# Combine and plot
library(ggplot2)
bind_rows(
mutate(vi1, model = "Model 1"),
mutate(vi2, model = "Model 2")
) %>%
ggplot(aes(x = reorder(Variable, Importance), y = Importance, fill = model)) +
geom_col(position = "dodge") +
coord_flip()