df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
pred = predict(m, newdata = dspace)
alluvial_model_response(pred, dspace, imp, degree = 3)
# partial dependency plotting method
if (FALSE) {
pred = get_pdp_predictions(df, imp
, .f_predict = randomForest:::predict.randomForest
, m
, degree = 3
, bins = 5)
alluvial_model_response(pred, dspace, imp, degree = 3, method = 'pdp')
}
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