Modifier and Type | Class and Description |
---|---|
class |
BayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
Modifier and Type | Class and Description |
---|---|
class |
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
BIFReader
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.
For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998). |
class |
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
Modifier and Type | Class and Description |
---|---|
class |
SimpleLogistic
Classifier for building linear logistic regression models.
|
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
Modifier and Type | Class and Description |
---|---|
class |
IBk
K-nearest neighbours classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
GridSearch
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). |
Modifier and Type | Class and Description |
---|---|
class |
DecisionTable
Class for building and using a simple decision table majority classifier.
For more information see: Ron Kohavi: The Power of Decision Tables. |
class |
DTNB
Class for building and using a decision table/naive bayes hybrid classifier.
|
class |
JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
PART
Class for generating a PART decision list.
|
class |
Ridor
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. |
Modifier and Type | Class and Description |
---|---|
class |
ADTree
Class for generating an alternating decision tree.
|
class |
BFTree
Class for building a best-first decision tree classifier.
|
class |
FT
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
|
class |
J48
Class for generating a pruned or unpruned C4.5 decision tree.
|
class |
J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
|
class |
LADTree
Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
|
class |
LMT
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
|
class |
M5P
M5Base.
|
class |
NBTree
Class for generating a decision tree with naive Bayes classifiers at the leaves.
For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. |
class |
RandomForest
Class for constructing a forest of random trees.
For more information see: Leo Breiman (2001). |
class |
REPTree
Fast decision tree learner.
|
class |
SimpleCart
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. |
Modifier and Type | Class and Description |
---|---|
class |
M5Base
M5Base.
|
Modifier and Type | Class and Description |
---|---|
class |
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference. |
class |
CoverTree
Class implementing the CoverTree datastructure.
The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor. |
class |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference. |
class |
LinearNNSearch
Class implementing the brute force search algorithm for nearest neighbour search.
|
class |
NearestNeighbourSearch
Abstract class for nearest neighbour search.
|
class |
PerformanceStats
The class that measures the performance of a nearest
neighbour search (NNS) algorithm.
|
class |
TreePerformanceStats
The class that measures the performance of a tree based
nearest neighbour search algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
AveragingResultProducer
Takes the results from a ResultProducer and submits
the average to the result listener.
|
class |
ClassifierSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a nominal class attribute.
|
class |
CostSensitiveClassifierSplitEvaluator
SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
|
class |
CrossValidationResultProducer
Generates for each run, carries out an n-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
DatabaseResultProducer
Examines a database and extracts out the results
produced by the specified ResultProducer and submits them to the specified
ResultListener.
|
class |
DensityBasedClustererSplitEvaluator
A SplitEvaluator that produces results for a density based clusterer.
|
class |
LearningRateResultProducer
Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset.
|
class |
RandomSplitResultProducer
Generates a single train/test split and calls the
appropriate SplitEvaluator to generate some results.
|
class |
RegressionSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a numeric class attribute.
|
Copyright © 2019 University of Waikato, Hamilton, NZ. All rights reserved.