K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting. For more information, see D. […]
Nearest-neighbour classifier. Uses normalized Euclidean distance to find the training instance closest to the given test instance, and predicts the same […]
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
Class for building and using a 0-R classifier. Predicts the mean (for a numeric class) or the mode (for a nominal class).
Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
Class for constructing a forest of random trees. For more information see: Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for […]
Learns a simple linear regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with […]
Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option to allow estimation of […]