Package: RWeka 0.4-46
RWeka: R/Weka Interface
An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package 'RWeka' contains the interface code, the Weka jar is in a separate package 'RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.
Authors:
RWeka_0.4-46.tar.gz
RWeka_0.4-46.zip(r-4.5)RWeka_0.4-46.zip(r-4.4)RWeka_0.4-46.zip(r-4.3)
RWeka_0.4-46.tgz(r-4.4-any)RWeka_0.4-46.tgz(r-4.3-any)
RWeka_0.4-46.tar.gz(r-4.5-noble)RWeka_0.4-46.tar.gz(r-4.4-noble)
RWeka_0.4-46.tgz(r-4.4-emscripten)RWeka_0.4-46.tgz(r-4.3-emscripten)
RWeka.pdf |RWeka.html✨
RWeka/json (API)
| # Install 'RWeka' in R: |
| install.packages('RWeka', repos = c('https://kurthornik.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:cadb071065. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | NOTE | Nov 02 2024 |
| R-4.5-linux | NOTE | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Nov 02 2024 |
Exports:AdaBoostM1AlphabeticTokenizerAprioriBaggingC45LoaderC45SaverCobwebCostSensitiveClassifierDBScanDecisionStumpDiscretizeevaluate_Weka_classifierFarthestFirstGainRatioAttributeEvalIBkInfoGainAttributeEvalIteratedLovinsStemmerJ48JRipLBRLinearRegressionlist_Weka_interfacesLMTLogisticLogitBoostLovinsStemmerM5PM5Rulesmake_Weka_associatormake_Weka_attribute_evaluatormake_Weka_classifiermake_Weka_clusterermake_Weka_filtermake_Weka_package_loaderMultiBoostABNGramTokenizerNormalizeOneRparse_Weka_digraphPARTread.arffSimpleKMeansSMOStackingTertiusWeka_controlWordTokenizerWOWWPMwrite_to_dotwrite.arffXMeansXRFFLoaderXRFFSaver
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create DOT Representations | write_to_dot write_to_dot.Weka_classifier |
| Model Statistics for R/Weka Classifiers | evaluate_Weka_classifier |
| Model Predictions for R/Weka Classifiers | fitted.Weka_classifier predict.Weka_classifier |
| Class Predictions for R/Weka Clusterers | predict.Weka_clusterer |
| Read Data from ARFF Files | read.arff read.arff.R |
| R/Weka Associators | Apriori Tertius |
| R/Weka Attribute Evaluators | GainRatioAttributeEval InfoGainAttributeEval |
| R/Weka Classifier Functions | LinearRegression Logistic SMO Weka_classifier_functions |
| R/Weka Lazy Learners | IBk LBR Weka_classifier_lazy |
| R/Weka Meta Learners | AdaBoostM1 Bagging CostSensitiveClassifier LogitBoost MultiBoostAB Stacking Weka_classifier_meta |
| R/Weka Rule Learners | JRip M5Rules OneR PART Weka_classifier_rules |
| R/Weka Classifier Trees | DecisionStump J48 LMT M5P parse_Weka_digraph plot.Weka_tree Weka_classifier_trees |
| R/Weka Classifiers | Weka_classifiers |
| R/Weka Clusterers | Cobweb DBScan FarthestFirst SimpleKMeans XMeans |
| Control Weka Options | as.character.Weka_control print.Weka_control Weka_control |
| R/Weka File Loaders and Savers | C45Loader C45Saver XRFFLoader XRFFSaver |
| R/Weka Filters | Discretize Normalize |
| R/Weka interfaces | list_Weka_interfaces make_Weka_associator make_Weka_attribute_evaluator make_Weka_classifier make_Weka_clusterer make_Weka_filter make_Weka_package_loader |
| R/Weka Stemmers | IteratedLovinsStemmer LovinsStemmer |
| R/Weka Tokenizers | AlphabeticTokenizer NGramTokenizer WordTokenizer |
| Weka Option Wizard | WOW |
| Weka Package Manager | WPM |
| Write Data into ARFF Files | write.arff write.arff.R |
