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A B C D E F G H I K L M N O P Q R S T U V W Z
| accident2014 | Sample of car accident location in the UK during year 2014. |
| ADABOOST | Classification using AdaBoost |
| alcohol | Alcohol dataset |
| APRIORI | Classification using APRIORI |
| apriori-class | APRIORI classification model |
| autompg | Auto MPG dataset |
| BAGGING | Classification using Bagging |
| beetles | Flea beetles dataset |
| birth | Birth dataset |
| boosting-class | Boosting methods model |
| boxclus | Clustering Box Plots |
| britpop | Population and location of 18 major british cities. |
| CA | Correspondence Analysis (CA) |
| CART | Classification using CART |
| cartdepth | Depth |
| cartinfo | CART information |
| cartleafs | Number of Leafs |
| cartnodes | Number of Nodes |
| cartplot | CART Plot |
| CDA | Classification using Canonical Discriminant Analysis |
| cda-class | Canonical Disciminant Analysis model |
| closegraphics | Close a graphics device |
| compare | Comparison of two sets of clusters |
| compare.accuracy | Comparison of two sets of clusters, using accuracy |
| compare.jaccard | Comparison of two sets of clusters, using Jaccard index |
| compare.kappa | Comparison of two sets of clusters, using kappa |
| confusion | Confuion matrix |
| cookies | Cookies dataset |
| cookies.desc.test | Cookies dataset |
| cookies.desc.train | Cookies dataset |
| cookies.y.test | Cookies dataset |
| cookies.y.train | Cookies dataset |
| cookplot | Plot the Cook's distance of a linear regression model |
| correlated | Correlated variables |
| cost.curves | Plot Cost Curves |
| credit | Credit dataset |
| data.diag | Square dataset |
| data.gauss | Gaussian mixture dataset |
| data.parabol | Parabol dataset |
| data.target1 | Target1 dataset |
| data.target2 | Target2 dataset |
| data.twomoons | Two moons dataset |
| data.xor | XOR dataset |
| data1 | "data1" dataset |
| data2 | "data2" dataset |
| data3 | "data3" dataset |
| dataset-class | Training set and test set |
| dbs-class | DBSCAN model |
| DBSCAN | DBSCAN clustering method |
| decathlon | Decathlon dataset |
| distplot | Plot a k-distance graphic |
| EM | Expectation-Maximization clustering method |
| em-class | Expectation-Maximization model |
| eucalyptus | Eucalyptus dataset |
| evaluation | Evaluation of classification or regression predictions |
| evaluation.accuracy | Accuracy of classification predictions |
| evaluation.fmeasure | F-measure |
| evaluation.fowlkesmallows | Fowlkes–Mallows index |
| evaluation.goodness | Goodness |
| evaluation.jaccard | Jaccard index |
| evaluation.kappa | Kappa evaluation of classification predictions |
| evaluation.msep | MSEP evaluation of regression predictions |
| evaluation.precision | Precision of classification predictions |
| evaluation.r2 | R2 evaluation of regression predictions |
| evaluation.recall | Recall of classification predictions |
| exportgraphics | Open a graphics device |
| exportgraphics.off | Toggle graphic exports |
| exportgraphics.on | Toggle graphic exports |
| factorial-class | Factorial analysis results |
| FEATURESELECTION | Classification with Feature selection |
| filter.rules | Filtering a set of rules |
| frequentwords | Frequent words |
| general.rules | Remove redundancy in a set of rules |
| getvocab | Extract words and phrases from a corpus |
| GRADIENTBOOSTING | Classification using Gradient Boosting |
| HCA | Hierarchical Cluster Analysis method |
| intern | Clustering evaluation through internal criteria |
| intern.dunn | Clustering evaluation through Dunn's index |
| intern.interclass | Clustering evaluation through interclass inertia |
| intern.intraclass | Clustering evaluation through intraclass inertia |
| ionosphere | Ionosphere dataset |
| keiser | Keiser rule |
| KERREG | Kernel Regression |
| KMEANS | K-means method |
| kmeans.getk | Estimation of the number of clusters for _K_-means |
| KNN | Classification using k-NN |
| knn-class | K Nearest Neighbours model |
| LDA | Classification using Linear Discriminant Analysis |
| leverageplot | Plot the leverage points of a linear regression model |
| LINREG | Linear Regression |
| linsep | Linsep dataset |
| loadtext | load a text file |
| LR | Classification using Logistic Regression |
| MCA | Multiple Correspondence Analysis (MCA) |
| MEANSHIFT | MeanShift method |
| meanshift-class | MeanShift model |
| MLP | Classification using Multilayer Perceptron |
| MLPREG | Multi-Layer Perceptron Regression |
| model-class | Generic classification or regression model |
| movies | Movies dataset |
| NB | Classification using Naive Bayes |
| NMF | Non-negative Matrix Factorization |
| ozone | Ozone dataset |
| params-class | Learning Parameters |
| PCA | Principal Component Analysis (PCA) |
| performance | Performance estimation |
| plot.cda | Plot function for cda-class |
| plot.factorial | Plot function for factorial-class |
| plot.som | Plot function for som-class |
| plotcloud | Plot word cloud |
| plotclus | Generic Plot Method for Clustering |
| plotdata | Advanced plot function |
| plotzipf | Plot rank versus frequency |
| POLYREG | Polynomial Regression |
| predict.apriori | Model predictions |
| predict.boosting | Model predictions |
| predict.cda | Model predictions |
| predict.dbs | Predict function for DBSCAN |
| predict.em | Predict function for EM |
| predict.kmeans | Predict function for K-means |
| predict.knn | Model predictions |
| predict.meanshift | Predict function for MeanShift |
| predict.model | Model predictions |
| predict.selection | Model predictions |
| predict.textmining | Model predictions |
| print.apriori | Print a classification model obtained by APRIORI |
| print.factorial | Plot function for factorial-class |
| pseudoF | Pseudo-F |
| QDA | Classification using Quadratic Discriminant Analysis |
| query.docs | Document query |
| query.words | Word query |
| RANDOMFOREST | Classification using Random Forest |
| reg1 | reg1 dataset |
| reg1.test | reg1 dataset |
| reg1.train | reg1 dataset |
| reg2 | reg2 dataset |
| reg2.test | reg2 dataset |
| reg2.train | reg2 dataset |
| regplot | Plot function for a regression model |
| resplot | Plot the studentized residuals of a linear regression model |
| roc.curves | Plot ROC Curves |
| rotation | Rotation |
| runningtime | Running time |
| scatterplot | Clustering Scatter Plots |
| selectfeatures | Feature selection for classification |
| selection-class | Feature selection |
| snore | Snore dataset |
| SOM | Self-Organizing Maps clustering method |
| som-class | Self-Organizing Maps model |
| SPECTRAL | Spectral clustering method |
| spectral-class | Spectral clustering model |
| spine | Spine dataset |
| spine.test | Spine dataset |
| spine.train | Spine dataset |
| splitdata | Splits a dataset into training set and test set |
| stability | Clustering evaluation through stability |
| STUMP | Classification using one-level decision tree |
| summary.apriori | Print summary of a classification model obtained by APRIORI |
| SVD | Singular Value Decomposition |
| SVM | Classification using Support Vector Machine |
| SVMl | Classification using Support Vector Machine with a linear kernel |
| SVMr | Classification using Support Vector Machine with a radial kernel |
| SVR | Regression using Support Vector Machine |
| SVRl | Regression using Support Vector Machine with a linear kernel |
| SVRr | Regression using Support Vector Machine with a radial kernel |
| temperature | Temperature dataset |
| TEXTMINING | Text mining |
| textmining-class | Text mining object |
| titanic | Titanic dataset |
| toggleexport | Toggle graphic exports |
| toggleexport.off | Toggle graphic exports |
| toggleexport.on | Toggle graphic exports |
| treeplot | Dendrogram Plots |
| TSNE | t-distributed Stochastic Neighbor Embedding |
| universite | University dataset |
| vectorize.docs | Document vectorization |
| vectorize.words | Word vectorization |
| vectorizer-class | Document vectorization object |
| vowels | Vowels dataset |
| vowels.test | Vowels dataset |
| vowels.train | Vowels dataset |
| wheat | Wheat dataset |
| wine | Wine dataset |
| zoo | Zoo dataset |