Added ORCiD ID to the author field in the
Way cooler logo?
The arguments to
plotPartial() are now more consistent with each other.
The names of (most) helper functions have changed from lowerCamelCase to snake_case.
Properly registered native routines and disabled symbol search.
Fixed a bug for
gbm models using the multinomial distribution.
Refactored code to improve structure.
partial() gained three new options:
center. The latter two have to do with constructing individual conditional expectation (ICE) curves and centered ICE (c-ICE) curves. The
inv.link option is for transforming predictions from models that can use non-Gaussian distributions (e.g.,
xgboost). Note that these options were added for convenience and the same results (plus much more) can still be obtained using the flexible
pred.fun argument. (#36).
Better support for neural networks from the
Fixed a bug for
nnet::multinom() models with binary response.
Fixed minor pandoc conversion issue with
Added subdirectory called
tools to hold figures for
partial. These arguments make it easier to construct PDPs over the relevant range of a numeric predictor without having to specify
pred.grid, especially when outliers are present in the predictors (which can distort the plotted relationship).
train argument can now accept matrices; in particular, object of class
"dgCMatrix". This is useful, for example, when working with XGBoost models (i.e., objects of class
New logical argument
prob indicating whether or not partial dependence values for classification problems should be returned on the original probability scale, rather than the centered logit; details for the centered logit can be found on page 370 in the second edition of The Elements of Statistical Learning.
Fixed some typos in
autoplot for automatically creating
ggplot2 graphics from
partial() is now much faster with
"gbm" object due to a call to
pred.grid is not explicitly given by the user. (
gbm::plot.gbm() exploits a computational shortcut that does not involve any passes over the training data.)
New (experimental) function
topPredictors() for extracting the names of the most “important” predictors. This should make it one step easier (in most cases) to construct PDPs for the most “important”" features in a fitted model.
A new argument,
pred.fun, allows the user to supply their own prediction function. Hence, it is possible to obtain PDPs based on the median, rather than the mean. It is also possible to obtain PDPs for classification problems on the probability scale. See
?partial for examples.
Minor bug fixes and documentation tweaks.
... argument in the call to
partial() now refers to additional arguments to be passed onto
stats::predict() rather than
plyr::aaply(). For example, using
"gbm" objects will require specification of
n.trees which can now simply be passed to
partial() via the
Added the following arguments to
plyr-based progress bars),
foreach-based parallel execution), and
paropts (list of additional arguments passed onto
parallel = TRUE).
Various bug fixes.
partial() now throws an informative error message when the
pred.grid argument refers to predictors not in the original training data.
The column name for the predicted value has been changed from
randomForest is no longer imported.
Added support for the
caret package (i.e., objects of class
Added example data sets:
boston (corrected Boston housing data) and
pima (corrected Pima Indians diabetes data).
Fixed error that sometimes occurred when
chull = TRUE causing the convex hull to not be computed.
plotPartial() to be more modular.
gbm support for most non-
Fixed a couple of URLs and typos.
Added more thorough documentation.
Added support for C5.0, Cubist, nonlinear least squares, and XGBoost models.