predefinedClassifiers {TunePareto}

TunePareto wrappers for certain classifiers

Description

Creates TunePareto classifier objects for the k-Nearest Neighbour classifier, support vector machines, and trees.

Usage

tunePareto.knn()
               
tunePareto.svm()
               
tunePareto.tree()
                
tunePareto.randomForest()

tunePareto.NaiveBayes()

Details

tunePareto.knn encapsulates a k-Nearest Neighbour classifier as defined in link[class]{knn} in package class. The classifier allows for supplying and tuning the following parameters of link[class]{knn}:

k, l, use.all

tunePareto.svm encapsulates the support vector machine svm classifier in package e1071. The classifier allows for supplying and tuning the following parameters:

kernel, degree, gamma, coef0, cost, nu, class.weights, cachesize, tolerance, epsilon, scale, shrinking, fitted, subset, na.action

tunePareto.tree encapsulates the CART classifier tree in package tree. The classifier allows for supplying and tuning the following parameters:

weights, subset, na.action, method, split, mincut, minsize, mindev

as well as the type parameter of predict.tree.

tunePareto.randomForest encapsulates the randomForest classifier in package randomForest. The classifier allows for supplying and tuning the following parameters:

subset, na.action, ntree, mtry, replace, classwt, cutoff, strata, sampsize, nodesize, maxnodes

tunePareto.NaiveBayes encapsulates the NaiveBayes classifier in package klaR. The classifier allows for supplying and tuning the following parameters:

prior, usekernel, fL, subset, na.action, bw, adjust, kernel, weights, window, width, give.Rkern, n, from, to, cut, na.rm

Value

Returns objects of class TuneParetoClassifier as described in tuneParetoClassifier. These can be passed to functions like tunePareto or trainTuneParetoClassifier.

See Also

tuneParetoClassifier, tunePareto, trainTuneParetoClassifier

Examples

# tune a k-NN classifier with different 'k' and 'l' 
# on the 'iris' data set
print(tunePareto(classifier = tunePareto.knn(),
                 data = iris[, -ncol(iris)], 
                 labels = iris[, ncol(iris)],
                 k = c(5,7,9),
                 l = c(1,2,3),
                 objectiveFunctions=list(cvError(10, 10),
                                         cvSpecificity(10, 10, caseClass="setosa"))))
                 
# tune an SVM with different costs on 
# the 'iris' data set
# using Halton sequences for sampling
print(tunePareto(classifier = tunePareto.svm(),
                 data = iris[, -ncol(iris)], 
                 labels = iris[, ncol(iris)],
                 cost = as.interval(0.001,10),
                 sampleType = "halton",
                 numCombinations=20,                 
                 objectiveFunctions=list(cvWeightedError(10, 10),
                                         cvSensitivity(10, 10, caseClass="setosa"))))

# tune a CART classifier with different 
# splitting criteria on the 'iris' data set
print(tunePareto(classifier = tunePareto.tree(),
                 data = iris[, -ncol(iris)], 
                 labels = iris[, ncol(iris)],
                 split = c("deviance","gini"),
                 objectiveFunctions=list(cvError(10, 10),
                                         cvErrorVariance(10, 10))))

# tune a Random Forest with different numbers of trees 
# on the 'iris' data set
print(tunePareto(classifier = tunePareto.randomForest(),
                 data = iris[, -ncol(iris)], 
                 labels = iris[, ncol(iris)],
                 ntree = seq(50,300,50),
                 objectiveFunctions=list(cvError(10, 10),
                                         cvSpecificity(10, 10, caseClass="setosa"))))

# tune a Naive Bayes classifier with different kernels
# on the 'iris' data set
print(tunePareto(classifier = tunePareto.NaiveBayes(),
                 data = iris[, -ncol(iris)], 
                 labels = iris[, ncol(iris)],
                 kernel = c("gaussian", "epanechnikov", "rectangular",
                            "triangular", "biweight",
                            "cosine", "optcosine"),
                 objectiveFunctions=list(cvError(10, 10),
                                         cvSpecificity(10, 10, caseClass="setosa"))))