parcats
requires an alluvial plot created with easyalluvial
to create an interactive parrallel categories diagram.
suppressPackageStartupMessages(require(dplyr)) suppressPackageStartupMessages(require(easyalluvial)) suppressPackageStartupMessages(require(parcats))
p <- alluvial_wide(mtcars2, max_variables = 5) parcats(p, marginal_histograms = TRUE, data_input = mtcars2)
Machine Learning models operate in a multidimensional space and their response is hard to visualise. Model response and partial dependency plots attempt to visualise ML models in a two dimensional space. Using alluvial plots or parrallel categories diagrams we can increase the number of dimensions.
Here we see the response of a random forest model if we vary the three variables with the highest importance while keeping all other features at their median/mode value.
df <- select(mtcars2, -ids ) m <- randomForest::randomForest( disp ~ ., df) imp <- m$importance dspace <- get_data_space(df, imp, degree = 3) pred <- predict(m, newdata = dspace) p <- alluvial_model_response(pred, dspace, imp, degree = 3) parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)
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