|
Class Summary |
| AdaBoostM1 |
Class for boosting a classifier using Freund & Schapire's Adaboost
M1 method. |
| AdditiveRegression |
Meta classifier that enhances the performance of a regression base
classifier. |
| AttributeSelectedClassifier |
Class for running an arbitrary classifier on data that has been reduced
through attribute selection. |
| Bagging |
Class for bagging a classifier. |
| BVDecompose |
Class for performing a Bias-Variance decomposition on any classifier
using the method specified in: |
| CheckClassifier |
Class for examining the capabilities and finding problems with
classifiers. |
| ClassificationViaRegression |
Class for doing classification using regression methods. |
| Classifier |
Abstract classifier. |
| CostMatrix |
Class for a misclassification cost matrix. |
| CostSensitiveClassifier |
This metaclassifier makes its base classifier cost-sensitive. |
| CVParameterSelection |
Class for performing parameter selection by cross-validation for any
classifier. |
| DecisionStump |
Class for building and using a decision stump. |
| DecisionTable |
Class for building and using a simple decision table majority classifier. |
| DistributionClassifier |
Abstract classification model that produces (for each test instance)
an estimate of the membership in each class
(ie. |
| DistributionMetaClassifier |
Class that wraps up a Classifier and presents it as a DistributionClassifier
for ease of programmatically handling Classifiers in general -- only the
one predict method (distributionForInstance) need be worried about. |
| Evaluation |
Class for evaluating machine learning models. |
| FilteredClassifier |
Class for running an arbitrary classifier on data that has been passed
through an arbitrary filter. |
| HyperPipes |
Class implementing a HyperPipe classifier. |
| IB1 |
IB1-type classifier. |
| IBk |
K-nearest neighbour classifier. |
| Id3 |
Class implementing an Id3 decision tree classifier. |
| KernelDensity |
Class for building and using a very simple kernel density classifier. |
| LinearRegression |
Class for using linear regression for prediction. |
| Logistic |
Class for building and using a two-class logistic regression model
with a ridge estimator. |
| LogitBoost |
Class for boosting any classifier that can handle weighted instances. |
| LWR |
Locally-weighted regression. |
| MetaCost |
This metaclassifier makes its base classifier cost-sensitive using the
method specified in |
| MultiClassClassifier |
Class for handling multi-class datasets with 2-class distribution
classifiers. |
| MultiScheme |
Class for selecting a classifier from among several using cross
validation on the training data. |
| NaiveBayes |
Class for a Naive Bayes classifier using estimator classes. |
| NaiveBayesSimple |
Class for building and using a simple Naive Bayes classifier. |
| OneR |
Class for building and using a 1R classifier. |
| Prism |
Class for building and using a PRISM classifier. |
| RegressionByDiscretization |
Class for a regression scheme that employs any distribution
classifier on a copy of the data that has the class attribute
discretized. |
| SMO |
Implements John C. |
| Stacking |
Implements stacking. |
| ThresholdSelector |
Class for selecting a threshold on a probability output by a
distribution classifier. |
| UserClassifier |
Class for generating an user defined decision tree. |
| VFI |
Class implementing the voting feature interval classifier. |
| VotedPerceptron |
Implements the voted perceptron algorithm by Freund and
Schapire. |
| ZeroR |
Class for building and using a 0-R classifier. |