root/MachineLearningWithMATLAB/Classification_RobotFailures/ReducingModelComplexity.m @ 11
| 10 | anderm8 | %% Improving Model Accuracy by Evaluating Architecture & Feature Choices
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% Most of the predictive models discussed here have numerous parameters
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% that can be selected to tweak the model to improve its performance for a
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% particular problem. Selecting the right features is also a very important
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% step in the machine learning workflow. This script explores methods of
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% making these decisions
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% Copyright 2015 The MathWorks, Inc.
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%% Start with the Bagged Decision Tree model
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% Import data
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faultData = importFaultData('faultData.xlsx');
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faultData.Fault = categorical(faultData.Fault);
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names =faultData.Properties.VariableNames;
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% Filter data
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faultData(faultData.Fault == 'lost',:) = [];
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faultData(faultData.Fault == 'moved',:) = [];
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faultData.Fault = removecats(faultData.Fault);
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%
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X = table2array(faultData(:,1:end-1));
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Y = faultData.Fault;
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retrain = true;
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opts = statset('UseParallel', true);
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tbL = TreeBagger(100,X,Y,'method','classification',...
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'Options',opts,'OOBVarImp','on');
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%% Estimating a Good Ensemble Size for Bagged Classification Trees
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% Because each tree in the bag is trained on a subset of the training data,
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% the out-of-bag observations can be used to estimate the out of sample
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% misclassification rate of the tree. Examining the out-of-bag error may
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% give an insight into determining a good ensemble size.
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clf
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plot(oobError(tbL)); grid on;
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xlabel('Number of Grown Trees');
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ylabel('Out-of-Bag Classification Error/Misclassification Probability');
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%% Estimating Feature Importance
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% Feature importance measures the increase in prediction error if the
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% values of that variable are permuted across the out-of-bag observations.
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figure;
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[~,idxvarimp] = sort(tbL.OOBPermutedVarDeltaError, 'ascend');
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barh(tbL.OOBPermutedVarDeltaError(idxvarimp));
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title('Out-Of-Bag Feature Importance');
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xlabel('Relative Feature Importance Score');
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set(gca,'YTick',1:length(names)-1,'YTickLabel', names(idxvarimp), 'XGrid', 'on');
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%% Sequential Feature Selection
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% Feature selection reduces the dimensionality of data by selecting only a
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% subset of measured features (predictor variables) to create a model.
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% Selection criteria involves the minimization of a specific measure of
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% predictive error for models fit to different subsets. In this case we try
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% to find the best feature group that minimizes the sum of false positive
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% and false negative rates.
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%
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% Sequential feature selection can be computationally intensive. It can
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% benefit significantly from parallel computing.
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gcp; % open a pool of workers
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opts = statset('display','iter','UseParallel','always','TolFun',1e-2);
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% keepin forces those columns to be used
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% use our cv partition object with 3-fold cross validation
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[fs,~] = sequentialfs(@featureTest,X,Y,...
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'options',opts);
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% Display
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disp(fs)
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disp(names(fs).')
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