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Binary options positive expectation mat scheme

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Of the 16 distinct labels, only 13 are represented in the response Y. Each label describes various degrees of arrhythmia, and You must specify at least three arguments: a method, a number of learners, and the type of learner. For this example, specify 'GentleBoost' for the method, for the number of learners, and a decision tree template that uses surrogate splits because there are missing observations.

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Most of its properties are empty, but the software fills them with their default values during training. Train a one-versus-all ECOC classifier using the ensembles of decision trees as binary learners.

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To speed up training, use binning and parallel computing. Binning 'NumBins',50 — When you have a large training data set, you can binary options positive expectation mat scheme up training a potential decrease in accuracy by using the 'NumBins' name-value pair argument.

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This argument is valid only when fitcecoc uses a tree learner. If you specify the 'NumBins' value, then the software bins every numeric predictor into a specified number of equiprobable bins, and then grows trees on the bin indices instead of the original data.

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You can try 'NumBins',50 first, and then change the 'NumBins' value depending on the accuracy and training speed. Parallel computing 'Options',statset 'UseParallel',true — With a Parallel Computing Toolbox license, you can speed up the computation by using parallel computing, which sends each binary learner to a worker in the pool.

The number of workers depends on your system configuration. Therefore, specifying the 'UseParallel' option is not helpful on a single computer.

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Use this option on a cluster. Connected to the parallel pool number of workers: 6. The warning indicates that some classes are not represented while the software trains at least one fold.

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Therefore, those folds cannot predict labels for the missing classes. You can inspect the results of a fold using cell indexing and dot notation.

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For example, access the results of the first fold by entering CVMdl. Use the cross-validated ECOC classifier to predict validation-fold labels. You can compute the confusion matrix by using confusionchart.

Move and resize the chart by changing the inner position property to ensure that the percentages appear in the row summary. Xbinned values are 0 for categorical predictors. Load the fisheriris data set.

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For reproducibility, set the random seed and use the 'expected-improvement-plus' acquisition function. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: Use linear binary learners for one of the models and kernel binary learners for the other. Compare the resubstitution classification error of the two models.

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In general, you can perform multiclass classification of tall data by using fitcecoc with linear or kernel binary learners. When you use fitcecoc to train a model on tall arrays, you cannot use SVM binary learners directly. However, you can use either linear or kernel binary classification models that use SVMs.

If you want to run the example using the local MATLAB session when you have Parallel Computing Toolbox, you can change the global execution environment by using the mapreducer function.

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Create a datastore that references the folder containing Fisher's iris data set. Specify 'NA' values as missing data so that datastore replaces them with NaN values.

Create tall versions of the predictor and response data. SepalLength t.

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