intubate (version 1.0.0)

adabag: Interfaces for adabag package for data science pipelines.

Description

Interfaces to adabag functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_autoprune(data, ...)
# ntbt_bagging(data, ...)  ## Already defined in ipred
ntbt_bagging.cv(data, ...)
ntbt_boosting(data, ...)
ntbt_boosting.cv(data, ...)

Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# library(adabag)
# 
# 
# ## ntbt_autoprune: Builds automatically a pruned tree of class rpart
# ## Original function to interface
# autoprune(Species ~ ., data = iris)
# 
# ## The interface puts data as first parameter
# ntbt_autoprune(iris, Species ~ .)
# 
# ## so it can be used easily in a pipeline.
# iris %>%
#   ntbt_autoprune(Species ~ .)
# 
# ## ntbt_bagging: Applies the Bagging algorithm to a data set
# library(rpart)
# data(iris)
# 
# ## Original function to interface
# bagging(Species ~ ., data = iris, mfinal = 10)
# 
# ## The interface puts data as first parameter
# ntbt_bagging(iris, Species ~ ., mfinal = 10)
# 
# ## so it can be used easily in a pipeline.
# iris %>%
#   ntbt_bagging(Species ~ ., mfinal = 10)
# 
# 
# 
# ## Original function to interface
# iris.baggingcv <- bagging.cv(Species ~ ., v = 2, data = iris, mfinal = 10,
#                              control = rpart.control(cp = 0.01))
# iris.baggingcv[-1]
# 
# ## The interface puts data as first parameter
# iris.baggingcv <- ntbt_bagging.cv(iris, Species ~ ., v = 2, mfinal = 10,
#                                   control = rpart.control(cp = 0.01))
# iris.baggingcv[-1]
# 
# ## so it can be used easily in a pipeline.
# iris.baggingcv <- iris %>%
#   ntbt_bagging.cv(Species ~ ., v = 2, mfinal = 10,
#                   control = rpart.control(cp = 0.01))
# iris.baggingcv[-1]
# 
# 
# ## ntbt_boosting: Applies the AdaBoost.M1 and SAMME algorithms to a data set
# ## Original function to interface
# boosting(Species ~ ., data = iris, boos = TRUE, mfinal = 5)
# 
# ## The interface puts data as first parameter
# ntbt_boosting(iris, Species ~ ., boos = TRUE, mfinal = 5)
# 
# ## so it can be used easily in a pipeline.
# iris %>%
#   ntbt_boosting(Species ~ ., boos = TRUE, mfinal = 5)
# 
# 
# ## ntbt_boosting.cv: Runs v-fold cross validation with AdaBoost.M1 or SAMME
# ## Original function to interface
# iris.boostcv <- boosting.cv(Species ~ ., v = 2, data = iris, mfinal = 10, 
#                             control = rpart.control(cp = 0.01))
# iris.boostcv[-1]
# 
# ## The interface puts data as first parameter
# iris.boostcv <- ntbt_boosting.cv(iris, Species ~ ., v = 2, mfinal = 10, 
#                                  control = rpart.control(cp = 0.01))
# iris.boostcv[-1]
# 
# ## so it can be used easily in a pipeline.
# iris.boostcv <- iris %>%
#   ntbt_boosting.cv(Species ~ ., v = 2, mfinal = 10, 
#                    control = rpart.control(cp = 0.01))
# iris.boostcv[-1]
# ## End(Not run)