| Package | Description |
|---|---|
| weka.classifiers.bayes | |
| weka.classifiers.functions | |
| weka.classifiers.lazy | |
| weka.classifiers.meta | |
| weka.classifiers.trees |
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveBayesMultinomialText
Multinomial naive bayes for text data.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive
Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SGD
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
|
class |
SGDText
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LWL
Locally weighted learning.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultiClassClassifierUpdateable
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HoeffdingTree
A Hoeffding tree (VFDT) is an incremental, anytime
decision tree induction algorithm that is capable of learning from massive
data streams, assuming that the distribution generating examples does not
change over time.
|
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