mlearning (version 1.2.1)

mlRpart: Supervised classification and regression using recursive partitioning

Description

Unified (formula-based) interface version of the recursive partitioning algorithm as implemented in rpart::rpart().

Usage

mlRpart(train, ...)
ml_rpart(train, ...)
# S3 method for formula
mlRpart(formula, data, ..., subset, na.action)
# S3 method for default
mlRpart(train, response, ..., .args. = NULL)
# S3 method for mlRpart
predict(
  object,
  newdata,
  type = c("class", "membership", "both"),
  method = c("direct", "cv"),
  ...
)

Value

ml_rpart()/mlRpart() creates an mlRpart, mlearning object containing the classifier and a lot of additional metadata used by the functions and methods you can apply to it like predict() or cvpredict(). In case you want to program new functions or extract specific components, inspect the "unclassed" object using unclass().

Arguments

train

a matrix or data frame with predictors.

...

further arguments passed to rpart::rpart() or its predict() method (see the corresponding help page.

formula

a formula with left term being the factor variable to predict (for supervised classification), a vector of numbers (for regression) and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the class ~ . short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by using I()).

data

a data.frame to use as a training set.

subset

index vector with the cases to define the training set in use (this argument must be named, if provided).

na.action

function to specify the action to be taken if NAs are found. For ml_rpart() na.fail is used by default. The calculation is stopped if there is any NA in the data. Another option is na.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For the predict() method, the default, and most suitable option, is na.exclude. In that case, rows with NAs in newdata= are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order as newdata=).

response

a vector of factor (classification) or numeric (regression).

.args.

used internally, do not provide anything here.

object

an mlRpart object

newdata

a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.

type

the type of prediction to return. "class" by default, the predicted classes. Other options are "membership" the membership (number between 0 and 1) to the different classes, or "both" to return classes and memberships,

method

"direct" (default) or "cv". "direct" predicts new cases in newdata= if this argument is provided, or the cases in the training set if not. Take care that not providing newdata= means that you just calculate the self-consistency of the classifier but cannot use the metrics derived from these results for the assessment of its performances. Either use a different data set in newdata= or use the alternate cross-validation ("cv") technique. If you specify method = "cv" then cvpredict() is used and you cannot provide newdata= in that case.

See Also

mlearning(), cvpredict(), confusion(), also rpart::rpart() that actually does the classification.

Examples

# Prepare data: split into training set (2/3) and test set (1/3)
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133)
iris_train <- iris[train, ]
iris_test <- iris[-train, ]
# One case with missing data in train set, and another case in test set
iris_train[1, 1] <- NA
iris_test[25, 2] <- NA

iris_rpart <- ml_rpart(data = iris_train, Species ~ .)
summary(iris_rpart)
# Plot the decision tree for this classifier
plot(iris_rpart, margin = 0.03, uniform = TRUE)
text(iris_rpart, use.n = FALSE)
# Predictions
predict(iris_rpart) # Default type is class
predict(iris_rpart, type = "membership")
predict(iris_rpart, type = "both")
# Self-consistency, do not use for assessing classifier performances!
confusion(iris_rpart)
# Cross-validation prediction is a good choice when there is no test set
predict(iris_rpart, method = "cv")  # Idem: cvpredict(res)
confusion(iris_rpart, method = "cv")
# Evaluation of performances using a separate test set
confusion(predict(iris_rpart, newdata = iris_test), iris_test$Species)