create model response plot for caret models
Wraps alluvial_model_response and
get_data_space into one call for caret models.
alluvial_model_response_caret(
train,
data_input,
degree = 4,
bins = 5,
bin_labels = c("LL", "ML", "M", "MH", "HH"),
col_vector_flow = c("#FF0065", "#009850", "#A56F2B", "#005EAA", "#710500", "#7B5380",
"#9DD1D1"),
method = "median",
parallel = FALSE,
params_bin_numeric_pred = list(bins = 5),
pred_train = NULL,
stratum_label_size = 3.5,
force = F,
resp_var = NULL,
...
)train |
caret train object |
data_input |
dataframe, input data |
degree |
integer, number of top important variables to select. For plotting more than 4 will result in two many flows and the alluvial plot will not be very readable, Default: 4 |
bins |
integer, number of bins for numeric variables, increasing this number might result in too many flows, Default: 5 |
bin_labels |
labels for the bins from low to high, Default: c("LL", "ML", "M", "MH", "HH") |
col_vector_flow, |
character vector, defines flow colours, Default: c('#FF0065','#009850', '#A56F2B', '#005EAA', '#710500') |
method, |
character vector, one of c('median', 'pdp')
. Default: 'median' |
parallel |
logical, turn on parallel processing for pdp method. Default: FALSE |
params_bin_numeric_pred |
list, additional parameters passed to
|
pred_train |
numeric vector, base the automated binning of the pred vector on the distribution of the training predictions. This is useful if marginal histograms are added to the plot later. Default = NULL |
stratum_label_size |
numeric, Default: 3.5 |
force |
logical, force plotting of over 1500 flows, Default: FALSE |
resp_var |
character, sometimes target variable cannot be inferred and needs to be passed. Default NULL |
... |
additional parameters passed to
|
this model visualisation approach follows the "visualising the model in the dataspace" principle as described in Wickham H, Cook D, Hofmann H (2015) Visualizing statistical models: Removing the blindfold. Statistical Analysis and Data Mining 8(4) <doi:10.1002/sam.11271>
ggplot2 object
We are using 'furrr' and the 'future' package to paralelize some of the computational steps for calculating the predictions. It is up to the user to register a compatible backend (see plan).
if(check_pkg_installed("caret")) {
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
train = caret::train( disp ~ .,
df,
method = 'rf',
trControl = caret::trainControl( method = 'none' ),
importance = TRUE )
alluvial_model_response_caret(train, df, degree = 3)
}
# partial dependency plotting method
## Not run:
future::plan("multisession")
alluvial_model_response_caret(train, df, degree = 3, method = 'pdp', parallel = TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.