knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
The aim of Classification and Regression Tests
is to make the process of running classifier and regression tests easier. Each classifier and regression technique available in R, such as Adaboost, Random Forests, or Classification and Regression Trees, seems to have slightly different requirements for the data, such as whether NA
s are allowed. Furthermore, each technique can have (slightly) different parameters for training a model and making predictions.
This package provides a single API for making predictions based on a regression model or classification model, that makes sure the data is cleaned in such a way that the technique can process it with a minimal amount of problems. The results of a test are presented in a consistent way.
Basically, the aim of crtests
is to make running a classifier or regression test as simple as providing data, selecting a technique. Several techniques are supported and verified 'out of the box': Random Forests for regression and classification through the randomForest
package; CART for regression and classification through the rpart
package; Adaboost for regression through the gbm
package and Adaboost for classification through the boosting
function from the adabag
package; linear regression through lm
.
crtests
splits the classifier/regression testing process into multiple steps, each of which can be adapted to other techniques with requirements of their own. See the "extending" vignette to see how crtests
can be made to work with your favorite technique, if it does not work out of the box.
crtests
can be used both for single tests, where a single sample of data is used as a training set, and for multiple tests, where multiple samples of training data are created, and a test is run on each. The latter supports cross validation.
library(crtests) library(randomForest) library(caret) data(iris) # A classification test test <- createtest(data = iris, dependent = "Species", problem = "classification", method = "randomForest", name = "An example classification test", train_index = sample(150, 100) ) runtest(test) # A regression test test <- createtest(data = iris, dependent = "Sepal.Width", problem = "regression", method = "randomForest", name = "An example regression test", train_index = sample(150,100) ) runtest(test)
library(crtests) library(randomForest) library(rpart) library(caret) library(stringr) # A classification multitest summary( multitest(data = iris, dependent = "Species", problem = "classification", method = "randomForest", name = "An example classification multitest", iterations = 10, cross_validation = TRUE, preserve_distribution = TRUE ) ) # A regression multitest summary( multitest(data = iris, dependent = "Sepal.Width", problem = "regression", method = "rpart", name = "An example regression multitest", iterations = 15, cross_validation = FALSE ) )
devtools
from CRAN: install.packages(devtools)
crtests
from GitHub:
devtools::install_github("sjoerdvds/crtests")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.