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

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

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)

Arguments

X	a vector (or a factor) of predictions (with two classes) or an object of class randomUniformForest (with OOB option enabled).
Y	a vector of numeric (integer) responses, or a factor, with two classes.
classes	a vector (or a factor) of values that designate the class.
positive	convention for the positive class (e.g. the minority class).
ranking.threshold	option currently implemented but not fully tested.
ranking.values	option currently implemented but not fully tested.
falseDiscoveryRate	if TRUE, precision-recall curve is plotted. if FALSE, default value, ROC curve is plotted.
plotting	plotting the ROC curve ?
printValues	display values to screen ?
Beta	'beta' value for F-beta score.

Author(s)

Saip Ciss [e-mail]

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)) 

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