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📍 📈 📝 Interactive Studio with Explanations for ML Predictive Models
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Interactive Studio with Explanations for ML Predictive Models

Project Status: Active – The project has reached a stable, usable state and is being actively developed. CRAN_Status_Badge Build Status Coverage Status


The modelStudio package automates explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of serverless HTML site.

It combines R with D3.js to produce plots and descriptions for local and global explanations. The whole is greater than the sum of its parts, so it also supports EDA (Exploratory Data Analysis) on top of that. modelStudio is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.

See a demoRead the vignette: modelStudio - perks and features

The modelStudio package is a part of the DrWhy.AI universe.


# Install from CRAN: 

# Install the development version from GitHub:


This package bases on DALEX explainers created with DALEX::explain().


Create a model:

titanic_small <- titanic_imputed[, c(1,2,3,6,7,9)]
titanic_small$survived <- titanic_small$survived == "yes"

model_titanic_glm <- glm(survived ~ gender + age + fare + class + sibsp,
                         data = titanic_small, family = "binomial")

Wrap it into an explainer:

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_small[,-6],
                               y = titanic_small[,6],
                               label = "glm")

Pick some data points:

new_observations <- titanic_small[1:4,]
rownames(new_observations) <- c("Lucas", "James", "Thomas", "Nancy")

Make a studio for the model:

modelStudio(explain_titanic_glm, new_observations)

More examples here.


You can save modelStudio using controls on the top of the RStudio Viewer or with r2d3::save_d3_html() and r2d3::save_d3_png().

Cheat Sheet



Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.

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