Of X or Tbl with the corresponding value in The software weighs the observations in each row 'Weights' and a numeric vector of positive values or the name ofĪ variable in Tbl. Observation weights, specified as the comma-separated pair consisting of For details, see Automatic Creation of Dummy Variables.ĭata Types: single | double | logical | char | string | cell For an ordered categorical variable, fitcsvm creates one less dummy variable than the number of categories. For an unordered categorical variable, fitcsvm creates one dummy variable for each level of the categorical variable. The CategoricalPredictors name-value argument.įor the identified categorical predictors, fitcsvm creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. To identify any other predictors as categorical predictors, specify them by using ( X), fitcsvm assumes that all predictors areĬontinuous. Predictor data is in a table ( Tbl), fitcsvmĪssumes that a variable is categorical if it is a logical vector, categorical vector, characterĪrray, string array, or cell array of character vectors. The names must match the entries in PredictorNames. String array or cell array of character vectorsĮach element in the array is the name of a predictor variable. Pad the names with extra blanks so each row of the character matrix has the same length. TheĮach row of the matrix is the name of a predictor variable. Observation weights variable, or any other variables that the function does not use.Ī true entry means that the corresponding predictor is categorical. TheĬategoricalPredictors values do not count the response variable, If fitcsvm uses a subset of input variables as predictors, then theįunction indexes the predictors using only the subset. P is the number of predictors used to train the model. The index values are between 1 and p, where | | result | | runtime | (observed) | (estim.) | | | | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | BoxConstraint| KernelScale | Support Vector Machines for Binary Classification.Karush-Kuhn-Tucker (KKT) Complementarity Conditions.Find Multiple Class Boundaries Using Binary SVM.Detect Outliers Using SVM and One-Class Learning.Train and Cross-Validate SVM Classifier.Plot Decision Boundary and Margin Lines for Two-Class SVM Classifier.Statistics and Machine Learning Toolbox.
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