randomUniformForest (version 1.1.2)

importance.randomUniformForest: Variables Importance for random Uniform Forests

Description

Compute object thats leads to a full analysis of features (importance, dependency, interactions, selection, ...).

Usage

## S3 method for class 'randomUniformForest':
importance(object,
	maxVar = 30,
	maxInteractions = 3,
	Xtest = NULL,
	predObject = NULL,
	\dots)
	## S3 method for class 'importance':
plot(x,
	nGlobalFeatures = 30,
	nLocalFeatures = 5,
	Xtest =	NULL,
	whichFeature = NULL,
	whichOrder = "all",
    outliersFilter = TRUE,
	formulaInput = NULL,
	border = NA,
	\dots)
	## S3 method for class 'importance':
print(x, \dots)

Value

• an object of class importance.

concept

• variable importance
• variable selection

Details

'Importance' retrieves global importance from 'object' which is "variable importance from the model and for training data". Then it computes, either for train or test data and for at least two orders : 1- interactions, which show a kind of dependence between two features, for all couples of variables. Interactions shows links between variables by using informations at the leaf level. Level of interactions is standardized between 0 and 1. Most important (highly predictive) features usually have more interactions, but a low predictive feature can have many interactions that gives insights about the big picture. 2- overall interactions, that lead to local variable importance which is "variable importance from the model and interactions between features". Local variable importance shows how response values can be explained, using more than just predictive features. 3 - partial dependence, that leads to show how response values are evolving according to one feature, and knowing distribution of all others. 4- variable importance over labels (in classification), that shows, on the same figure, how each important feature is matching labels from the view point of the model. Features with influence are the most discriminant over labels. In regression, object is called "Dependence on most important predictors". Summarized, importance object tells (or leads to, using others functions) "what, how and when" features affect the response. Note that importance depends of hyper-parameters of the model (mtry, depth, nodesize, ...) and one has to take care. Categorical features (and all others variables or types of variable) are treated by randomUniformForest() function as numeric values (default option). Accuracy is usually not affected, but their importance has to be assessed more carefully, using all objects provided by importance() and dependence functions (see examples, for a case study summary).

See Also

partialDependenceOverResponses, partialDependenceBetweenPredictors, partialImportance

Examples

## not run 
## NOTES: please remove comments to run, since importance and plot method will draw many graphics,
## use of option 'threads = 1' (disabling parallel processing) to speed up computing, 
## since parallel processing is slower for small sample.
## 1 - Importance for Classification and Regression with formula
####  Classification

# data(iris)
# iris.ruf <- randomUniformForest(Species ~ ., data = iris, threads = 1)

## global importance: giving 2 ways to explain importance (table and visualization)
# summary(iris.ruf)

## much more about importance : giving 4 ways to explain importance in a complementary fashion 
## of global importance
# iris.ruf.importance <- importance(iris.ruf, Xtest = iris, threads = 1)

## get importance summary
# iris.ruf.importance

## visualizing all in one
# plot(iris.ruf.importance, Xtest = iris)

## get details about observations (link between observations and features) : 
## note that class and features are replaced by their internal numeric values.
## Retrieve model of observations importance
# iris.observationsImportance <- 
# data.frame(iris.ruf.importance$localVariableImportance$obsVariableImportance)

## first impute true labels
## iris.observationsImportance$class <- as.factor(iris.observationsImportance$class)
# levels(iris.observationsImportance$class) <- iris.ruf$variables

## draw a sample of 10 observations to see which features affect the most every observation 
## and the label associated (either from train or test sample)
# iris.observationsImportance[sample(nrow(iris), 10), ]

#### Regression

# data(airquality)
# airquality.data = airquality

## impute NA
# airquality.NAimputed <- fillNA2.randomUniformForest(airquality.data)

## compute model
# ozone.ruf <- randomUniformForest(Ozone ~ ., data = airquality.NAimputed, threads = 1)
# ozone.ruf

## print and plot (global) variable importance
# summary(ozone.ruf)

## much more about importance
# ozone.ruf.importance <- importance(ozone.ruf, Xtest = airquality.NAimputed, threads = 1)

## visualizing all in one : in case of formula, 'formulaInput' is needed for the 'plot' method
# plot(ozone.ruf.importance, Xtest = airquality.NAimputed, formulaInput = ozone.ruf$formula)

## 2- Importance for Classification and Regression without formula (more usual and recommended 
# for random Uniform Forests)

#### 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 then test in the same function
# car.ruf <- randomUniformForest(car.data.train, car.class.train,
# xtest = car.data.test, ytest = car.class.test, threads = 1)
# car.ruf

## global importance: note that 'safety' does not appear to be an important feature in the barplot
## but in the table, it is, by far, the most important feature of unacceptable (unacc) cars.
# summary(car.ruf)

## interactions and local importance tell most of the story...
## (not run)
# car.ruf.importance <- importance(car.ruf, Xtest = car.data.train, threads = 1)

## ...that can be used to explain train data
# plot(car.ruf.importance, Xtest = car.data.train)

## or explain test data
# car.ruf.importance.test <- importance(car.ruf, Xtest = car.data.test, threads = 1)
# plot(car.ruf.importance.test, Xtest = car.data.test)

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

# data(ConcreteCompressiveStrength)
# 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", threads = 1)
# Concrete.ruf

## predictions : option ' type = "all" ' is needed to manually assess importance of a test set 
# Concrete.ruf.pred <- predict(Concrete.ruf, Concrete.data.test, type = "all")

## more options about importance : more interactions
# Concrete.ruf.importance <- importance(Concrete.ruf, Xtest = Concrete.data.test,
# maxInteractions = 6, predObject = Concrete.ruf.pred, threads = 1)

## and more features
# plot(Concrete.ruf.importance, nLocalFeatures = 7, Xtest = Concrete.data.test)