functions
This category contains 18 nodes.
GaussianProcesses (3.6) (legacy)
Implements Gaussian Processes for regression without hyperparameter-tuning. For more information see David J.C. Mackay (1998). Introduction to Gaussian […]
IsotonicRegression (3.6) (legacy)
Learns an isotonic regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with […]
LeastMedSq (3.6) (legacy)
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions. Least squared regression […]
LibLINEAR (3.6) (legacy)
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, […]
LibSVM (3.6) (legacy)
A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster […]
LinearRegression (3.6) (legacy)
Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
Logistic (3.6) (legacy)
Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper […]
MultilayerPerceptron (3.6) (legacy)
A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both. The network can also be […]
PaceRegression (3.6) (legacy)
Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the […]
PLSClassifier (3.6) (legacy)
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.