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 |
augmentation | Duplicate and add noise to a dataset |
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 |
cookplot | Plot the Cook's distance of a linear regression model |
correlated | Correlated variables |
cost.curves | Plot Cost Curves |
credit | Credit dataset |
data1 | "data1" dataset |
data2 | "data2" dataset |
data3 | "data3" dataset |
data.diag | Square dataset |
data.gauss | Gaussian mixture dataset |
data.parabol | Parabol dataset |
dataset-class | Training set and test set |
data.target1 | Target1 dataset |
data.target2 | Target2 dataset |
data.twomoons | Two moons dataset |
data.xor | XOR dataset |
DBSCAN | DBSCAN clustering method |
dbs-class | DBSCAN model |
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.adjr2 | Adjusted R2 evaluation of regression 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 |
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 |
kaiser | Kaiser 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 |
plotavsp | Plot actual vs. predictions |
plot.cda | Plot function for cda-class |
plotcloud | Plot word cloud |
plotclus | Generic Plot Method for Clustering |
plotdata | Advanced plot function |
plot.factorial | Plot function for factorial-class |
plot.som | Plot function for som-class |
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 |
reg2 | 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 |
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... |
SVMr | Classification using Support Vector Machine with a radial... |
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 |
treeplot | Dendrogram Plots |
TSNE | t-distributed Stochastic Neighbor Embedding |
universite | University dataset |
vectorize.docs | Document vectorization |
vectorizer-class | Document vectorization object |
vectorize.words | Word vectorization |
vowels | Vowels dataset |
wheat | Wheat dataset |
wine | Wine dataset |
zoo | Zoo dataset |
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