Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper […]
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. Cohen […]
Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each […]
Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves […]
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less […]
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all […]
Executes a JPython script, taking 1 input table and returning 1 output table.
Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, […]
Class implementing the FP-growth algorithm for finding large item sets without candidate generation. Iteratively reduces the minimum support until it finds […]
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance […]