Predictive value and negative predictive value is calculated using the The functions requires that the factors have exactly the same levels.įor two class problems, the sensitivity, specificity, positive The vector should have names corresponding to the classes. Otherwise, it should be a vector of numeric values with elements for each class. When data has two levels, prevalence should be a single numeric value. If there are only two factor levels, the first level will be used as the "positive" result.Ī character vector of dimnames for the tableĪ numeric value or matrix for the rate of the "positive" class of the data. )Ī factor of predicted classes (for the default method) or an object of class table.Ī factor of classes to be used as the true resultsĪn optional character string for the factor level that corresponds to a "positive" result (if that makes sense for your data). ) # S3 method for class 'table' confusionMatrix ( data, positive = NULL, prevalence = NULL. ) # Default S3 method: confusionMatrix ( data, reference, positive = NULL, dnn = c ( "Prediction", "Reference" ), prevalence = NULL.
plotObsVsPred: Plot Observed versus Predicted Results in Regression and.ĬonfusionMatrix ( data.plotClassProbs: Plot Predicted Probabilities in Classification Models.pcaNNet: Neural Networks with a Principal Component Step.panel.needle: Needle Plot Lattice Panel.panel.lift: Lattice Panel Functions for Lift Plots.oil: Fatty acid composition of commercial oils.nullModel: Fit a simple, non-informative model.2Reference: Quantile Normalization to a Reference Distribution.
normalize2Reference: Quantile Normalize Columns of a Matrix Based on a Reference.nearZeroVar: Identification of near zero variance predictors.models: A List of Available Models in train.modelLookup: Tools for Models Available in 'train'.mdrr: Multidrug Resistance Reversal (MDRR) Agent Data.maxDissim: Maximum Dissimilarity Sampling.lattice.rfe: Lattice functions for plotting resampling results of.lattice.resamples: Lattice Functions for Visualizing Resampling Results.: Lattice Functions for Visualizing Resampling Differences.lattice: Lattice functions for plotting resampling results.knn3: k-Nearest Neighbour Classification.: Generate Expression Values from Probes.format.bagEarth: Format 'bagEarth' objects.findLinearCombos: Determine linear combinations in a matrix.findCorrelation: Determine highly correlated variables.filterVarImp: Calculation of filter-based variable importance.featurePlot: Wrapper for Lattice Plotting of Predictor Variables.extractPrediction: Extract predictions and class probabilities from train.
dummyVars: Create A Full Set of Dummy Variables.downSample: Down- and Up-Sampling Imbalanced Data.: Create a dotplot of variable importance values.diff.resamples: Inferential Assessments About Model Performance.dhfr: Dihydrofolate Reductase Inhibitors Data.createDataPartition: Data Splitting functions.ain: Estimate a Resampled Confusion Matrix.confusionMatrix: Create a confusion matrix.classDist: Compute and predict the distances to class centroids.cars: Kelly Blue Book resale data for 2005 model year GM cars.calibration: Probability Calibration Plot.BoxCoxTrans: Box-Cox and Exponential Transformations.avNNet: Neural Networks Using Model Averaging.as.nfusionMatrix: Save Confusion Table Results.
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