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parsnip 0.1.2
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@topepo topepo released this 03 Jul 17:22
v0.1.2
81957b3

Breaking Changes

  • 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)

Other Changes

  • 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)

New Features

  • 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)

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