parsnip v0.1.1


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A Common API to Modeling and Analysis Functions

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).

Functions in parsnip

Name Description
boost_tree General Interface for Boosted Trees
mlp General Interface for Single Layer Neural Network
mars General Interface for MARS
linear_reg General Interface for Linear Regression Models
keras_mlp Simple interface to MLP models via keras
model_spec Model Specification Information
model_fit Model Fit Object Information
multi_predict Model predictions across many sub-models
logistic_reg General Interface for Logistic Regression Models
model_printer Print helper for model objects
multinom_reg General Interface for Multinomial Regression Models
get_model_env Working with the parsnip model environment
nearest_neighbor General Interface for K-Nearest Neighbor Models
fit.model_spec Fit a Model Specification to a Dataset
surv_reg General Interface for Parametric Survival Models
show_call Print the model call
predict_class.model_fit Other predict methods.
has_multi_predict Tools for models that predict on sub-models
tidy.model_fit Turn a parsnip model object into a tidy tibble
set_args Change elements of a model specification
translate Resolve a Model Specification for a Computational Engine
tidy.nullmodel Tidy method for null models
eval_args Evaluate parsnip model arguments
svm_poly General interface for polynomial support vector machines
rand_forest General Interface for Random Forest Models
null_model General Interface for null models
rpart_train Decision trees via rpart
predict.model_fit Model predictions
set_engine Declare a computational engine and specific arguments
svm_rbf General interface for radial basis function support vector machines
make_classes Prepend a new class
varying A placeholder function for argument values
varying_args.model_spec Determine varying arguments
type_sum.model_spec Succinct summary of parsnip object
nullmodel Fit a simple, non-informative model
set_new_model Tools to Register Models
reexports Objects exported from other packages
xgb_train Boosted trees via xgboost
check_empty_ellipse Check to ensure that ellipses are empty
add_rowindex Add a column of row numbers to a data frame
decision_tree General Interface for Decision Tree Models
control_parsnip Control the fit function
descriptors Data Set Characteristics Available when Fitting Models
convert_args Make a table of arguments
convert_stan_interval Convenience function for intervals
null_value Functions required for parsnip-adjacent packages
C5.0_train Boosted trees via C5.0
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