NEWS for pdp package
Changes for version 0.6.0
- 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 cetered ICE (c-ICE) curves. The
inv.link option is for transforming predictions from models that can use non-Gaussian distibutions (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).
plotPartial gained five new options:
?plotPartial for details.
- Fixed default y-axis label for
autoplot with two numeric predictors (#48).
- Better support for neuaral networks from the
- Fixed a bug for
nnet::multinom models with binary response.
Changes for version 0.5.2
- Fixed minor pandoc conversion issue with
- Added subdirectory called
tools to hold figures for
Changes for version 0.5.1
- Registered native routines and disabled symbol search.
Changes for version 0.5.0
- Added support for
- New arguments
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
- New function
autoplot for automatically creating
ggplot2 graphics from
Changes for version 0.4.0
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.
Changes for version 0.3.0
... 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
Changes for version 0.2.0
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-
Changes for version 0.1.0
randomForest is now imported.
- Added examples.
Changes for version 0.0.6
- Fixed a non canonical CRAN URL in the README file.
Changes for version 0.0.5
partial now makes sure each column of
pred.grid has the correct class, levels, etc.
partial gained a new option,
levelplot, which defaults to
TRUE. The original option,
contour, has changed and now specifies whether or not to add contour lines whenever
levelplot = TRUE.
Changes for version 0.0.4
- Fixed a number of URLs.
- More thorough documentation.
Changes for version 0.0.2
- Fixed a couple of URLs and typos.
- Added more thorough documentation.
- Added support for C5.0, Cubist, nonlinear least squares, and XGBoost models.
Changes for version 0.0.1