randomUniformForest (version 1.1.2)

roc.curve: ROC and precision-recall curves for random Uniform Forests

Description

plot ROC and precision-recall curves for objects of class randomUniformForest and compute F-beta score. It also works for any other model that provides predicted labels (but only for ROC curve).

Usage

roc.curve(X, Y, classes,
	positive = classes[2],
	ranking.threshold = 0,
	ranking.values = 0,
	falseDiscoveryRate = FALSE,
	plotting = TRUE,
	printValues = TRUE,
	Beta = 1)

See Also

importance.randomUniformForest

Examples

## Classification : "breast cancer" data 
# http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

data(breastCancer)
breastCancer.data <- breastCancer

# remove ID (first column) and divide data in train and test set
breastCancer.data = breastCancer.data[,-1]

n <- nrow(breastCancer.data)
p <- ncol(breastCancer.data)

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

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

# rename class in benign (class 2) and malignant (class 4) to have a better view
levels(breastCancer.class.train) = c("benign", "malignant")

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

levels(breastCancer.class.test) = c("benign", "malignant")

# compute model : train then test in the same function and assign class weights 
# to better match the distribution (OOB errors and Breiman's bounds can help to choose weights)
# Note that in this case 'recall' (or sensitivity) is the objective, 
# e.g. match all possible cases of malignant tumours even if false positive rate increase 
#(in this latter case, further steps will reveal the truth). If malignant tumour is not detected, 
# then diagnosis error is, by far, more critical.
breastCancer.ruf  <- randomUniformForest(breastCancer.data.train, breastCancer.class.train, 
xtest = breastCancer.data.test, ytest = breastCancer.class.test, 
classwt = c(1, 3.5), threads = 2, ntree = 40, BreimanBounds = FALSE)

# get a summary of model
breastCancer.ruf

## plot ROC Curve for test data
# roc.curve(breastCancer.ruf, breastCancer.class.test, levels(breastCancer.class.test)) 

## plot precision-recall curve for test data
# roc.curve(breastCancer.ruf, breastCancer.class.test, levels(breastCancer.class.test),
# falseDiscoveryRate = TRUE)

## associate cut-off and purely random forest as an alternative to find maximum malignant cases 
## with a low false positive rate. 
## 'classcutoff' option is a bit tricky. Let's take the example we will use below.
## classcutoff = c("benign", 1.25) means that number of votes for class "benign" 
## will be weighted by 0.4 (= Cte/1.25, where Cte = 0.5) for each response.
## Hence "benign" will never have majority unless it has 2.5 (1.25/0.5) times more votes 
## than "malignant" class and all votes sum to total number of trees.
# breastCancer.cutOff.ruf <- randomUniformForest(breastCancer.data.train, breastCancer.class.train, 
# xtest = breastCancer.data.test, ytest = breastCancer.class.test, classcutoff = c("benign", 1.25), 
# randomfeature = TRUE, ntree = 50, threads = 2, BreimanBounds = FALSE)

# roc.curve(breastCancer.cutOff.ruf, breastCancer.class.test, levels(breastCancer.class.test)) 
# roc.curve(breastCancer.cutOff.ruf, breastCancer.class.test, levels(breastCancer.class.test),
# falseDiscoveryRate = TRUE)

## evaluate OOB data, when there is no test set
# breastCancer.ruf <- randomUniformForest(breastCancer.data.train, breastCancer.class.train,
# classwt = c(1, 3.5), threads = 2)
# roc.curve(breastCancer.ruf, breastCancer.class.train, levels(breastCancer.class.train))