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fit_model() now treats numeric or character class
labels as classes; previously a numeric response (such as Y
in data69_1) was fitted as a regression.fit_model() only checks the columns used in the
formula, so missing values in unrelated columns no longer stop
fitting.boundary_compute() can recompute a multi-model result,
and recomputing a boundary no longer repeats the
"classbound_boundary" class.plot_boundary(type = "disagreement") no longer fails
when obs_data is supplied.boundary_explore_nd() rescales features before its
k-nearest-neighbour fallback for models without class probabilities, as
classifly does; previously one large-scale feature could dominate and
boundary points were close to random. It also keeps non-syntactic
feature names and accepts character columns.fit_model() evaluates transformations in the formula
(such as log(x) or I(x^2)) with the formula
interface; previously the classifier failed with “object not found”. The
features of the model are the original variables.fit_model() now stops with a clear message when the
response has fewer than two classes, instead of failing inside the
classifier.plot_boundary() with obs_data and
classbound() accept a projection basis without row names
(such as one taken directly from tourr), assuming the rows
follow the order of the training features, instead of failing.classbound() takes the axes from the fitted model, so
formulas containing transformations work.print() on a classbound object prints the
fitted model on separate lines instead of a literal
\n.tidymodels_bridge() now passes
resolution on.supports_prob = TRUE, and
fitted parsnip models from the workspace can show a probability
surface.zip program
(often missing on Windows).data.csv has an is_outlier column so
the script redraws the outliers as diamonds, as in the app.lda and qda models from ‘MASS’ are
supported out of the box, with their posterior probabilities available
for the probability surface.boundary_explore_nd() credits the ‘classifly’ package
and Wickham, Cook and Hofmann (2015).data69_1 help page now states that Y
is numeric with values 0, 1, 2 and that the data are the full UCI
dataset.predict_adapter() and boundary_workflow_set(),
and Ignacio Alvarez-Castro’s e-mail address.classbound(), and tests of the Shiny
application’s server logic with shiny::testServer().These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.