Implementation of the SVM-Maj Algorithm


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Documentation for package ‘SVMMaj’ version 0.2.9.3

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auc Returns the area under the curve value
AusCredit Australian Credit Approval Dataset
AusCredit.te Australian Credit Approval Dataset
AusCredit.tr Australian Credit Approval Dataset
classification Show the classification performance
diabetes Pima Indians Diabetes Data Set
diabetes.te Pima Indians Diabetes Data Set
diabetes.tr Pima Indians Diabetes Data Set
getHinge Hinge error function of SVM-Maj
isb I-spline basis of each column of a given matrix
isplinebasis Transform a given data into I-splines
normalize Normalize/standardize the columns of a matrix
plot.hinge Plot the hinge function
plot.svmmaj Print Svmmaj class
plot.svmmajcrossval Plot the cross validation output
plotWeights Plot the weights of all attributes from the trained SVM model
predict.svmmaj Out-of-Sample Prediction from Unseen Data.
predict.transDat Perform the transformation based on predefined settings
print.hinge Hinge error function of SVM-Maj
print.q.svmmaj SVM-Maj Algorithm
print.summary.svmmaj Print Svmmaj class
print.svmmaj Print Svmmaj class
print.svmmajcrossval Print SVMMaj cross validation results
roccurve Plot the ROC curve of the predicted values
summary.svmmaj Print Svmmaj class
summary.svmmajcrossval Print SVMMaj cross validation results
supermarket1996 Supermarket data 1996
svmmaj SVM-Maj Algorithm
svmmaj.default SVM-Maj Algorithm
svmmajcrossval k-fold Cross-Validation of SVM-Maj
transformdata Transform the data with normalization and/or spline basis
voting Congressional Voting Records Data Set
voting.te Congressional Voting Records Data Set
voting.tr Congressional Voting Records Data Set
X.svmmaj Returns transformed attributes