how to change crossvalind to cvpartition
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kav
am 8 Nov. 2019
Kommentiert: Anmol Dhiman
am 13 Nov. 2019
Hello,
What changes should i make to below code, to use cvpartition for cross validation?
TREES = [2 4 6 8 10 20 40:20:80 100:50:300 400 500];
FEATURES = [1:size(X1,2)]; % Breiman's rule: round(sqrt(size(X, 2)),0)
grid_AUC_crossval = zeros(length(TREES), length(FEATURES)); % to store train AUC scores
grid_F1_crossval = zeros(length(TREES), length(FEATURES));
for t=1:length(TREES)
for f=1:length(FEATURES)
trees = TREES(t);
features = FEATURES(f);
% run cross-validation on every model iteration
numFolds = 10;
Indices = crossvalind('Kfold', y1, numFolds);
final_preds = [];
final_scores = [];
yT = [];
for i = 1:numFolds
X2_fold = X1(Indices == i, :);
X1_fold = X1(Indices ~= i, :);
y1_fold = y1(Indices ~= i, :);
testIdx = (Indices == i); % index numbers of test items
Mdl = TreeBagger(trees, X1_fold, y1_fold, 'NumPredictorsToSample', features,'MinLeafSize', 5, 'Method', 'classification');
[preds, scores] = predict(Mdl, X2_fold);
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Anmol Dhiman
am 11 Nov. 2019
cvpartition is used for creating cross validation partition of the data.
You can use in the method as shown below
TREES = [2 4 6 8 10 20 40:20:80 100:50:300 400 500];
FEATURES = [1:size(X1,2)]; % Breiman's rule: round(sqrt(size(X, 2)),0)
X1 = meas;
grid_AUC_crossval = zeros(length(TREES), length(FEATURES)); % to store train AUC scores
grid_F1_crossval = zeros(length(TREES), length(FEATURES));
y1 = species;
for t=1:length(TREES)
for f=1:length(FEATURES)
trees = TREES(t);
features = FEATURES(f);
% run cross-validation on every model iteration
numFolds = 10;
c = cvpartition(y1,'KFold',numFolds);
final_preds = [];
final_scores = [];
yT = [];
for i = 1:numFolds
idx = training(c,i); % get indices for all trainings data
testIdx = (idx ~= i); % index numbers of test items
X2_fold = X1(idx ~= i, :);
X1_fold = X1(idx == i, :);
y1_fold = y1(idx == i, :);
Mdl = TreeBagger(trees, X1_fold, y1_fold, 'NumPredictorsToSample', features,'MinLeafSize', 5, 'Method', 'classification');
[preds, scores] = predict(Mdl, X2_fold);
end
end
end
4 Kommentare
Anmol Dhiman
am 13 Nov. 2019
You may change the number of tree to check the accuracies that you are getting, besides it will take less time for getting the results if you use less number of trees. Also you may try using parfor instead of for loop for decreasing the total time.
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