A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).
One issue with different functions available in R that do the same thing is that they can have different interfaces and arguments. For example, to fit a random forest classification model, we might have:
rf_1 <- randomForest(x, y, mtry = 12, ntree = 2000, importance = TRUE)# From rangerrf_2 <- ranger(y ~ .,data = dat,mtry = 12,num.trees = 2000,importance = 'impurity')# From sparklyrrf_3 <- ml_random_forest(dat,intercept = FALSE,response = "y",features = names(dat)[names(dat) != "y"],col.sample.rate = 12,num.trees = 2000)
Note that the model syntax is very different and that the argument names (and formats) are also different. This is a pain if you go between implementations.
In this example,
The idea of
parsnip is to:
ranger::rangeror other specific packages.
trees) so that users can remember a single name. This will help across model types too so that
treeswill be the same argument across random forest as well as boosting or bagging.
Using the example above, the
parsnip approach would be
rand_forest(mtry = 12, trees = 2000) %>%set_engine("ranger", importance = 'impurity') %>%fit(y ~ ., data = dat)
The engine can be easily changed and the mode can be determined when
fit is called. To use Spark, the change is simple:
rand_forest(mtry = 12, trees = 2000) %>%set_engine("spark") %>%fit(y ~ ., data = dat)
To install it, use:
First CRAN release
set_engine. There is no
othershas been replaced by
regularizationwas changed to
penaltyin a few models to be consistent with this change.
earthpackage will need to be attached to be fully operational.
newdatawas changed to
predict_rawmethod was added.