Node Connectivity
There are 99 nodes that can be used as predessesor
for a node with an input port of type 
Weka 3.6 Classifier.
NaiveBayesSimple (3.6) (legacy)
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution. For more information, see Richard […]
NaiveBayesUpdateable (3.6) (legacy)
Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes. This classifier will use a default precision of […]
WAODE (3.6) (legacy)
WAODE contructs the model called Weightily Averaged One-Dependence Estimators. For more information, see L. Jiang, H. Zhang: Weightily Averaged […]
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 […]