Hyperparameter Response Surface Employed Models and associated hyperparameters

Bayes Net

estimator	SimpleEstimator
searchAlgorithm	HillClimber, K2, LAGDHillClimber, TabuSearch, TAN
scoreType	BAYES, BDeu, MDL, AIC

Naive Bayes

Logistic

maxIts	10, 50, 100, 200, 500, 1000
ridge	0, 1E-8, 1E-7, 1E-6, 1E-5, 1E-4, 1E-3, 1E-2, 0.1, 0.5, 1, 5, 10

MultiLayerPerceptron

hiddenLayers	1
learningRate	0.001, 0.01, 0.1, 0.25, 0.5
momentum	0.001, 0.01, 0.1, 0.25, 0.5
trainingTime	10, 50, 100, 250, 500, 1000
validationSetSize	0, 2, 4, 6, 8, 10
validationThreshold	5, 10, 20, 50, 100

SMO

c	0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32
kernel	PolyKernel, RBFKernel, Puk
gamma	0.001, 0.01, 0.1, 0.25, 0.5
omega	0.0001, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 2, 5, 10, 20, 50, 100, 1000
sigma	0.0001, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 2, 5, 10, 20, 50, 100, 1000
degree	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
useLowerOrder	True, False

SimpleLogistic

weightTrimBeta	0, 0.0001, 0.001, 0.01
heuristicStop	0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100
maxBoostingIterations	100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
useAIC	True, False
useCrossValidation	True, False

IBk

KNN	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50
distanceWeighting	No distance weighting, Weight by 1/distance, Weight by 1-distance

KStar

globalBlend	0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100
entropicAutoBlend	True, False

DecisionTable

search	BestFirst, GreedyStepwise
conservativeForward	True, False
direction	Forward, Backward
searchTermination	1, 2, 3, 4, 5, 6, 7, 8, 9, 10

JRip

folds	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
minNo	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
optimizations	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
usePruning	True, False

OneR

minBucketSize	1, 2, ..., 50

PART

confidenceFactor	0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4
minNumObj	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
numFolds	2, 3, 4, 5, 6, 7, 8, 9, 10
Pruning	ReducedErrorPruning, Pruning, Unpruned

ZeroR

DecisionStump

J48

confidenceFactor	0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4
minNumObj	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
numFolds	2, 3, 4, 5, 6, 7, 8, 9, 10
Pruning	ReducedErrorPruning, Pruning, Unpruned
subtreeRaising	True, False
useLaplace	True, False

LMT

convertNominal	True, False
minNumInstances	2, 4, 6, 8, 10, 12, 15, 17, 20, 25, 50
splitOnResiduals	True, False
useAIC	True, False
weightTrimBeta	0, 0.0001, 0.001, 0.01

RepTree

minNum	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
minVarianceProb	0, 1E-8, 1E-7, 1E-6, 1E-5, 1E-4, 1E-3, 1E-2, 0.1, 0.5, 1
numFolds	2, 3, 4, 5, 6, 7, 8, 9, 10
noPruning	True, False

RandomForest

maxDepth	0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
numFeatures	0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
numTrees	10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 500

RandomTree

maxDepth	0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
minNum	0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10
numFold	0, 2, 3, 4, 5

Employed Classification Data Sets

australian	automobile	balance
banana	bands	breast-cancer
bupa	car	cleveland
credit-g	crx	dermatology
diabetes	ecoli	fourclass
german-numer	haberman	hayes-roth
heart	hepatitis	housevotes
ionosphere	iris	karhunen
kr-vs-kp	led7digit	liver-disorders
lymphography	mammographic	molecular-biology
monk-2	morphological	movement_libras
newthyroid	page-blocks	phoneme
pima	post-operative	saheart
segment	solar-flare	sonar-scale
spectfheart	svmguide2	svmguide3
svmguide4	tae	tic-tac-toe
titanic	vehicle	vowel
wdbc	wine	winequality-red
winequality-white	wisconsin	yeast
zernike	zoo