partialImportance: Partial Importance for random Uniform Forests

In randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning

Partial Importance for random Uniform Forests

Description

Describe what features are the most important for one specifically class (in case of classification) or explain features that are affecting, the most, variability of the response (for regression), either for train or test sample.

Usage

partialImportance(X, object, 
	whichClass = NULL,
	threshold = NULL,
	thresholdDirection = c("low", "high"),
	border = NA,
	nLocalFeatures = 5)

Arguments

X	a matrix or data frame specifying test (or train) data.
object	an object of class importance.
whichClass	for classification only. The index of the class that needs to be computed.
threshold	for regression only. The threshold point. Partial importance will compute importance below or above this point (see below).
thresholdDirection	where importance does it need to be fitted ? "low" will be lower than 'threshold' and "high" will be above.
border	visualization option. Draw border around barplots ?
nLocalFeatures	how many features does it need to be assessed ?

Details

Partial importance must be used in conjunction with importance object, since it explains what features have influence on a class (or on the variability of the response) but not how (that can be lower, higher or all values of one or many features). Partial Importance produces final (and local) rules that lead to the decision (observed or predicted class, observed or predicted value in cas of regression).

Value

A vector containing relative influence of the most important features in a decreasing order.

Author(s)

Saip Ciss [e-mail]

Examples

## not run 
## NOTE : please remove comments to run 
#### Classification: "car evaluation" data (http://archive.ics.uci.edu/ml/datasets/Car+Evaluation)

# data(carEvaluation)
# car.data = carEvaluation 

# n <- nrow(car.data)
# p <- ncol(car.data)

# trainTestIdx <- cut(sample(1:n, n), 2, labels= FALSE)

## train examples
# car.data.train <- car.data[trainTestIdx == 1, -p]
# car.class.train <- as.factor(car.data[trainTestIdx == 1, p])

## test data
# car.data.test <- car.data[trainTestIdx == 2, -p]
# car.class.test <- as.factor(car.data[trainTestIdx == 2, p])

## compute model : train and predict in the same function
# car.ruf <- randomUniformForest(car.data.train, car.class.train, xtest = car.data.test)
# car.ruf

## compute importance object deeper (increasing level of interactions) on test data
# car.ruf.importance <- importance(car.ruf, Xtest = car.data.test,
# maxinteractions = 6, threads = 2)

## get partial importance for the three most important features 
## in test set for "unacceptable" cars
# car.ruf.partialImportance.unacc <- partialImportance(car.data.test, 
# car.ruf.importance, whichClass = "unacc")
									
## get partial importance for the three most important features in test set for "acceptable" cars
# car.ruf.partialImportance.acc <- partialImportance(car.data.test, car.ruf.importance, 
# whichClass = "acc")									

#### Regression : "Concrete Compressive Strength" data
## (http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength)

# data(ConcreteCompressiveStrength.data)
# ConcreteCompressiveStrength.data = ConcreteCompressiveStrength

# n <- nrow(ConcreteCompressiveStrength.data)
# p <- ncol(ConcreteCompressiveStrength.data)

# trainTestIdx <- cut(sample(1:n, n), 2, labels= FALSE)

## train examples
# concrete.data.train <- ConcreteCompressiveStrength.data[trainTestIdx == 1, -p]
# concrete.responses.train <- ConcreteCompressiveStrength.data[trainTestIdx == 1, p]

## test data
# concrete.data.test <- ConcreteCompressiveStrength.data[trainTestIdx == 2, -p]
# concrete.responses.test <- ConcreteCompressiveStrength.data[trainTestIdx == 2, p]

## model
# concrete.ruf <- randomUniformForest(concrete.data.train, concrete.responses.train,
# featureselectionrule = "L1")
# concrete.ruf

## importance on train data
# concrete.ruf.importance <- importance.randomUniformForest(concrete.ruf, 
# Xtest = concrete.data.train, maxInteractions = 8, threads = 2)

## partial importance for concrete compressive strength higher than 40.
# concrete.ruf.partialImportance <- partialImportance(concrete.data.train, 
# concrete.ruf.importance, threshold = 40, thresholdDirection = "high")

## partial importance for concrete compressive strength lower than 30.
# concrete.ruf.partialImportance <- partialImportance(concrete.data.train, 
# concrete.ruf.importance, threshold = 30, thresholdDirection = "low")

randomUniformForest documentation built on June 22, 2022, 1:05 a.m.