Question

分类学习者Gui提供了导出代码的选项，如下所示

function [trainedClassifier, validationAccuracy] = trainClassifier(datasetTable)
% Convert input to table
datasetTable = table(datasetTable);
datasetTable.Properties.VariableNames = {'column'};
% Split matrices in the input table into vectors
datasetTable.column_1 = datasetTable.column(:,1);
datasetTable.column_2 = datasetTable.column(:,2);
datasetTable.column_3 = datasetTable.column(:,3);
datasetTable.column_4 = datasetTable.column(:,4);
datasetTable.column_5 = datasetTable.column(:,5);
datasetTable.column_6 = datasetTable.column(:,6);
datasetTable.column_7 = datasetTable.column(:,7);
datasetTable.column_8 = datasetTable.column(:,8);
datasetTable.column_9 = datasetTable.column(:,9);
datasetTable.column_10 = datasetTable.column(:,10);
datasetTable.column_11 = datasetTable.column(:,11); 
datasetTable.column_12 = datasetTable.column(:,12);
datasetTable.column_13 = datasetTable.column(:,13);
datasetTable.column_14 = datasetTable.column(:,14);
datasetTable.column_15 = datasetTable.column(:,15);
datasetTable.column_16 = datasetTable.column(:,16);
datasetTable.column_17 = datasetTable.column(:,17);
datasetTable.column_18 = datasetTable.column(:,18);
datasetTable.column_19 = datasetTable.column(:,19);
datasetTable.column = [];
% Extract predictors and response
predictorNames = {'column_1', 'column_2', 'column_3', 'column_4',      'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18'};
predictors = datasetTable(:,predictorNames);
predictors = table2array(varfun(@double, predictors));
response = datasetTable.column_19;
% Train a classifier
trainedClassifier = fitctree(predictors, response, 'PredictorNames',   {'column_1' 'column_2' 'column_3' 'column_4' 'column_5' 'column_6' 'column_7' 'column_8' 'column_9' 'column_10' 'column_11' 'column_12' 'column_13' 'column_14' 'column_15' 'column_16' 'column_17' 'column_18'}, 'ResponseName', 'column_19', 'ClassNames', [0 1], 'SplitCriterion', 'gdi', 'MaxNumSplits', 20, 'Surrogate', 'off');

% Perform cross-validation
partitionedModel = crossval(trainedClassifier, 'KFold', 5);

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

%% Uncomment this section to compute validation predictions and scores:
% % Compute validation predictions and scores
% [validationPredictions, validationScores] = kfoldPredict(partitionedModel);

现在，我想传递给trainClassifier一个不同大小的datasetTable并称之为：

trainClassifier(datasetTable,tablesize)

所以，有一个for - 循环来填充datasetTable.column_i和predictorNames。我没有使用过表格的经验，所以我没有设法写出语法正确的东西。

我认为predictorNames中的字符串可以使用

创建

eval(sprintf('column_%d ', i));

那么，你能对datasetTable的变量提出什么建议？

Matlab：重构分类学习者代码，用于处理变体大小的数据集

0 个答案: