trainClassifier.m

function [trainedClassifier, validationAccuracy, validationPredictions, validationScores] = trainClassifier(trainingData)
% trainClassifier(trainingData)
%  returns a trained classifier and its accuracy.
%  This code recreates the classification model trained in
%  Classification Learner app.
%
%   Input:
%       trainingData: the training data of same data type as imported
%        in the app (table or matrix).
%
%   Output:
%       trainedClassifier: a struct containing the trained classifier.
%        The struct contains various fields with information about the
%        trained classifier.
%
%       trainedClassifier.predictFcn: a function to make predictions
%        on new data. It takes an input of the same form as this training
%        code (table or matrix) and returns predictions for the response.
%        If you supply a matrix, include only the predictors columns (or
%        rows).
%
%       validationAccuracy: a double containing the accuracy in
%        percent. In the app, the History list displays this
%        overall accuracy score for each model.
%
%  Use the code to train the model with new data.
%  To retrain your classifier, call the function from the command line
%  with your original data or new data as the input argument trainingData.
%
%  For example, to retrain a classifier trained with the original data set
%  T, enter:
%    [trainedClassifier, validationAccuracy] = trainClassifier(T)
%
%  To make predictions with the returned 'trainedClassifier' on new data T,
%  use
%    yfit = trainedClassifier.predictFcn(T)
%
%  To automate training the same classifier with new data, or to learn how
%  to programmatically train classifiers, examine the generated code.

% Auto-generated by MATLAB on 23-Mar-2018 14:23:58


% Extract predictors and response
% This code processes the data into the right shape for training the
% classifier.
inputTable = trainingData;
predictorNames = {...
    'imMat1', 'imMat2', 'imMat3', 'imMat4', 'imMat5', 'imMat6', 'imMat7', 'imMat8', 'imMat9', ...
    'imMat10', 'imMat11', 'imMat12', 'imMat13', 'imMat14', 'imMat15', 'imMat16', 'imMat17', 'imMat18', 'imMat19', 'imMat20', ...
    'imMat21', 'imMat22', 'imMat23', 'imMat24', 'imMat25', 'imMat26', 'imMat27', 'imMat28', 'imMat29', 'imMat30', 'imMat31', 'imMat32', 'imMat33', 'imMat34', 'imMat35', 'imMat36', 'imMat37', 'imMat38', 'imMat39', 'imMat40', ...
    'imMat41', 'imMat42', 'imMat43', 'imMat44', 'imMat45', 'imMat46', 'imMat47'...
    };
predictors = inputTable(:, predictorNames);
response = inputTable.Var48;
isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];

% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationKNN = fitcknn(...
    predictors, ...
    response, ...
    'Distance', 'Euclidean', ...
    'Exponent', [], ...
    'NumNeighbors', 1, ...
    'DistanceWeight', 'Equal', ...
    'Standardize', true, ...
    'ClassNames', {'cyst'; 'prcc'});

% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
knnPredictFcn = @(x) predict(classificationKNN, x);
trainedClassifier.predictFcn = @(x) knnPredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct
trainedClassifier.RequiredVariables = {...
    'imMat1', 'imMat2', 'imMat3', 'imMat4', 'imMat5', 'imMat6', 'imMat7', 'imMat8', 'imMat9', ...
    'imMat10', 'imMat11', 'imMat12', 'imMat13', 'imMat14', 'imMat15', 'imMat16', 'imMat17', 'imMat18', 'imMat19', 'imMat20', ...
    'imMat21', 'imMat22', 'imMat23', 'imMat24', 'imMat25', 'imMat26', 'imMat27', 'imMat28', 'imMat29', 'imMat30', 'imMat31', 'imMat32', 'imMat33', 'imMat34', 'imMat35', 'imMat36', 'imMat37', 'imMat38', 'imMat39', 'imMat40', ...
    'imMat41', 'imMat42', 'imMat43', 'imMat44', 'imMat45', 'imMat46', 'imMat47'...
    };
trainedClassifier.ClassificationKNN = classificationKNN;
trainedClassifier.About = 'This struct is a trained classifier exported from Classification Learner R2016b.';
trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n  yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedClassifier''. \n \nThe table, T, must contain the variables returned by: \n  c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');

% Perform cross-validation
partitionedModel = crossval(trainedClassifier.ClassificationKNN, 'KFold', 10);

% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

% Compute validation predictions and scores
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);