root/MachineLearningWithMATLAB/Classification_RobotFailures/trainClassifierComplexTree.m @ 11
| 10 | anderm8 | function [trainedClassifier, validationAccuracy, validationPredictions, validationScores] = trainClassifierComplexTree(trainingData)
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% trainClassifier(trainingData)
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% returns a trained classifier and its validation accuracy.
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% This code recreates the classification model trained in
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% Classification Learner app.
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%
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% Input:
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% trainingData: the training data of same data type as imported
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% in the app (table or matrix).
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%
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% Output:
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% trainedClassifier: a struct containing the trained classifier.
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% The struct contains various fields with information about the
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% trained classifier.
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%
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% trainedClassifier.predictFcn: a function to make predictions
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% on new data. It takes an input of the same form as this training
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% code (table or matrix) and returns predictions for the response.
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% If you supply a matrix, include only the predictors columns (or
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% rows).
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%
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% validationAccuracy: a double containing the validation accuracy
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% score in percent. In the app, the History list displays this
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% overall accuracy score for each model.
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%
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% Use the code to train the model with new data.
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% To retrain your classifier, call the function from the command line
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% with your original data or new data as the input argument trainingData.
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%
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% For example, to retrain a classifier trained with the original data set
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% T, enter:
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% [trainedClassifier, validationAccuracy] = trainClassifier(T)
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%
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% To make predictions with the returned 'trainedClassifier' on new data T,
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% use
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% yfit = trainedClassifier.predictFcn(T)
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%
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% To automate training the same classifier with new data, or to learn how
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% to programmatically train classifiers, examine the generated code.
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% Auto-generated by MATLAB on 08-Sep-2015 01:13:03
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inputTable = trainingData;
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% Extract predictors and response
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% This code processes the data into the right shape for training the
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% classifier.
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predictorNames = {'Fx', 'Fy', 'Fz', 'Tx', 'Ty', 'Tz'};
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predictors = inputTable(:, predictorNames);
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response = inputTable.Fault;
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% Train a classifier
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% This code specifies all the classifier options and trains the classifier.
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classificationTree = fitctree(...
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predictors, ...
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response, ...
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'SplitCriterion', 'gdi', ...
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'MaxNumSplits', 100, ...
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'Surrogate', 'off', ...
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'ClassNames', categorical({'collision'; 'moved'; 'normal'; 'obstruction'}));
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trainedClassifier.ClassificationTree = classificationTree;
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extractPredictorsFromTableFcn = @(t) t(:, predictorNames);
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predictorExtractionFcn = @(x) extractPredictorsFromTableFcn(x);
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treePredictFcn = @(x) predict(classificationTree, x);
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trainedClassifier.predictFcn = @(x) treePredictFcn(predictorExtractionFcn(x));
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inputTable = trainingData;
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% Extract predictors and response
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% This code processes the data into the right shape for training the
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% classifier.
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predictorNames = {'Fx', 'Fy', 'Fz', 'Tx', 'Ty', 'Tz'};
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predictors = inputTable(:, predictorNames);
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response = inputTable.Fault;
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% Perform cross-validation
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partitionedModel = crossval(trainedClassifier.ClassificationTree, 'KFold', 5);
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% Compute validation accuracy
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validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');
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% Compute validation predictions and scores
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[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
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