An RStudio add-in is availble that makes writing multiple

`parsnip`

model specifications to the source window. It can be accessed via the IDE addin menus or by calling`parsnip_addin()`

.For

`xgboost`

models, users can now pass`objective`

to`set_engine("xgboost")`

. (#403)Changes to test for cases when CRAN cannot get

`xgboost`

to work on their Solaris configuration.There is now an

`augument()`

method for fitted models. See`augment.model_fit`

. (#401)Column names for

`x`

are now required when`fit_xy()`

is used. (#398)

`show_engines()`

will provide information on the current set for a model.For three models (

`glmnet`

,`xgboost`

, and`ranger`

), enable sparse matrix use via`fit_xy()`

(#373).Some added protections were added for function arguments that are dependent on the data dimensions (e.g.,

`mtry`

,`neighbors`

,`min_n`

, etc). (#184)Infrastructure was improved for running

`parsnip`

models in parallel using PSOCK clusters on Windows.

A

`glance()`

method for`model_fit`

objects was added (#325)Specific

`tidy()`

methods for`glmnet`

models fit via`parsnip`

were created so that the coefficients for the specific fitted`parsnip`

model are returned.

`glmnet`

models were fitting two intercepts (#349)The various

`update()`

methods now work with engine-specific parameters.

`parsnip`

now has options to set specific types of predictor encodings for different models. For example,`ranger`

models run using`parsnip`

and`workflows`

do the same thing by*not*creating indicator variables. These encodings can be overridden using the`blueprint`

options in`workflows`

. As a consequence, it is possible to get a different model fit that previous versions of`parsnip`

. More details about specific encoding changes are below. (#326)

`tidyr`

>= 1.0.0 is now required.SVM models produced by

`kernlab`

now use the formula method (see breaking change notice above). This change was due to how`ksvm()`

made indicator variables for factor predictors (with one-hot encodings). Since the ordinary formula method did not do this, the data are passed as-is to`ksvm()`

so that the results are closer to what one would get if`ksmv()`

were called directly.MARS models produced by

`earth`

now use the formula method.For

`xgboost`

, a one-hot encoding is used when indicator variables are created.Under-the-hood changes were made so that non-standard data arguments in the modeling packages can be accommodated. (#315)

A new main argument was added to

`boost_tree()`

called`stop_iter`

for early stopping. The`xgb_train()`

function gained arguments for early stopping and a percentage of data to leave out for a validation set.If

`fit()`

is used and the underlying model uses a formula, the*actual*formula is pass to the model (instead of a placeholder). This makes the model call better.A function named

`repair_call()`

was added. This can help change the underlying models`call`

object to better reflect what they would have obtained if the model function had been used directly (instead of via`parsnip`

). This is only useful when the user chooses a formula interface and the model uses a formula interface. It will also be of limited use when a recipes is used to construct the feature set in`workflows`

or`tune`

.The

`predict()`

function now checks to see if required modeling packages are installed. The packages are loaded (but not attached). (#249) (#308) (tidymodels/workflows#45)The function

`req_pkgs()`

is a user interface to determining the required packages. (#308)

`liquidSVM`

was added as an engine for`svm_rbf()`

(#300)

- The error message for missing packages was fixed (#289 and #292)

- S3 dispatch for
`tidy()`

was broken on R 4.0.

- A bug (#206 and #234) was fixed that caused an error when predicting with a multinomial
`glmnet`

model.

`glmnet`

was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrain`parsnip`

to that same requirement. All`glmnet`

tests are run locally.A set of internal functions are now exported. These are helpful when creating a new package that registers new model specifications.

`nnet`

was added as an engine to`multinom_reg()`

#209

- There were some mis-mapped parameters (going between
`parsnip`

and the underlying model function) for`spark`

boosted trees and some`keras`

models. See 897c927.

The time elapsed during model fitting is stored in the

`$elapsed`

slot of the parsnip model object, and is printed when the model object is printed.Some default parameter ranges were updated for SVM, KNN, and MARS models.

The model

`udpate()`

methods gained a`parameters`

argument for cases when the parameters are contained in a tibble or list.`fit_control()`

is soft-deprecated in favor of`control_parsnip()`

.

A bug was fixed standardizing the output column types of

`multi_predict`

and`predict`

for`multinom_reg`

.A bug was fixed related to using data descriptors and

`fit_xy()`

.A bug was fixed related to the column names generated by

`multi_predict()`

. The top-level tibble will always have a column named`.pred`

and this list column contains tibbles across sub-models. The column names for these sub-model tibbles will have names consistent with`predict()`

(which was previously incorrect). See 43c15db.A bug was fixed standardizing the column names of

`nnet`

class probability predictions.

Test case update due to CRAN running extra tests (#202)

Unplanned release based on CRAN requirements for Solaris.

The method that

`parsnip`

stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in`?get_model_env`

and the package vignette for adding models.The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.

For

`surv_reg()`

, the engine that uses the`survival`

package is now called`survival`

instead of`survreg`

.For

`glmnet`

models, the full regularization path is always fit regardless of the value given to`penalty`

. Previously, the model was fit with passing`penalty`

to`glmnet`

’s`lambda`

argument and the model could only make predictions at those specific values. (#195)

`add_rowindex()`

can create a column called`.row`

to a data frame.If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.

`nearest_neighbor()`

gained a`multi_predict`

method. The`multi_predict()`

documentation is a little better organized.A suite of internal functions were added to help with upcoming model tuning features.

A

`parsnip`

object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.

Small release driven by changes in `sample()`

in the current r-devel.

A “null model” is now available that fits a predictor-free model (using the mean of the outcome for regression or the mode for classification).

`fit_xy()`

can take a single column data frame or matrix for`y`

without error

`varying_args()`

now has a`full`

argument to control whether the full set of possible varying arguments is returned (as opposed to only the arguments that are actually varying).`fit_control()`

not returns an S3 method.For classification models, an error occurs if the outcome data are not encoded as factors (#115).

The prediction modules (e.g.

`predict_class`

,`predict_numeric`

, etc) were de-exported. These were internal functions that were not to be used by the users and the users were using them.An event time data set (

`check_times`

) was included that is the time (in seconds) to run`R CMD check`

using the "r-devel-windows-ix86+x86_64` flavor. Packages that errored are censored.

`varying_args()`

now uses the version from the`generics`

package. This means that the first argument,`x`

, has been renamed to`object`

to align with generics.For the recipes step method of

`varying_args()`

, there is now error checking to catch if a user tries to specify an argument that*cannot*be varying as varying (for example, the`id`

) (#132).`find_varying()`

, the internal function for detecting varying arguments, now returns correct results when a size 0 argument is provided. It can also now detect varying arguments nested deeply into a call (#131, #134).For multinomial regression, the

`.pred_`

prefix is now only added to prediction column names once (#107).For multinomial regression using glmnet,

`multi_predict()`

now pulls the correct default penalty (#108).Confidence and prediction intervals for logistic regression were only computed the intervals for a single level. Both are now computed. (#156)

First CRAN release

- The engine, and any associated arguments, are now specified using
`set_engine()`

. There is no`engine`

argument

- Arguments to modeling functions are now captured as quosures.
`others`

has been replaced by`...`

- Data descriptor names have beemn changed and are now functions. The descriptor definitions for “cols” and “preds” have been switched.

`regularization`

was changed to`penalty`

in a few models to be consistent with this change.- If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
- The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some
`predict`

methods, the`earth`

package will need to be attached to be fully operational.

- To be consistent with
`snake_case`

,`newdata`

was changed to`new_data`

. - A
`predict_raw`

method was added.

- A package dependency suffered a new change.

- The
`fit`

interface was previously used to cover both the x/y interface as well as the formula interface. Now,`fit()`

is the formula interface and`fit_xy()`

is for the x/y interface. - Added a
`NEWS.md`

file to track changes to the package. `predict`

methods were overhauled to be consistent.- MARS was added.