mlKnn {mlearning}

Supervised classification using k-nearest neighbor

Description

Unified (formula-based) interface version of the k-nearest neighbor algorithm provided by class::knn().

Usage

mlKnn(train, ...)

ml_knn(train, ...)

## S3 method for class 'formula'
mlKnn(formula, data, k.nn = 5, ..., subset, na.action)

## Default S3 method:
mlKnn(train, response, k.nn = 5, ...)

## S3 method for class 'mlKnn'
summary(object, ...)

## S3 method for class 'summary.mlKnn'
print(x, ...)

## S3 method for class 'mlKnn'
predict(
  object,
  newdata,
  type = c("class", "prob", "both"),
  method = c("direct", "cv"),
  na.action = na.exclude,
  ...
)

Arguments

train

a matrix or data frame with predictors.

...

further arguments passed to the classification method or its predict() method (not used here for now).

formula

a formula with left term being the factor variable to predict 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.

k.nn

k used for k-NN number of neighbor considered. Default is 5.

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_knn() 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 for the classification.

x, object

an mlKnn 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 "prob" the "probability" for the different classes as assessed by the number of neighbors of these classes, or "both" to return classes and "probabilities",

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.

Value

ml_knn()/mlKnn() creates an mlKnn, 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().

See Also

mlearning(), cvpredict(), confusion(), also class::knn() and ipred::predict.ipredknn() that actually do 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_knn <- ml_knn(data = iris_train, Species ~ .)
summary(iris_knn)
predict(iris_knn) # This object only returns classes
# Self-consistency, do not use for assessing classifier performances!
confusion(iris_knn)
# Use an independent test set instead
confusion(predict(iris_knn, newdata = iris_test), iris_test$Species)