Node Connectivity
There are 5406 nodes that can be used as predessesor
for a node with an input port of type 
Generic Port.
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 […]
PLSClassifier (3.6) (legacy)
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
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 […]
RBFNetwork (3.6) (legacy)
Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and […]
SMO (3.6) (legacy)
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all […]
SMOreg (3.6) (legacy)
SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting […]
SPegasos (3.6) (legacy)
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation […]
SimpleLinearRegression (3.6) (legacy)
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 […]