Is there a way to holdout specific data?

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Mark
Mark 2024-9-23
评论： Udit06 2024-9-23
I'm producing decision trees (both classification and regression) of my dataset and I wish to use a specific set of data as the training and a specific set for the testing. Is there a way to do this?
For example, say my dataset is consists of 100 rows, is there a way to tell the software to compute rows 1-75 as the training set and rows 76-100 as the test set?
Thanks in advance

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回答（1 个）

Udit06
Udit06 2024-9-23
Hi Mark,
You can use the array indexing to specify the training and testing sets using indexes. Please find below the code snippet to achieve the same:
% Define the training and testing indices
trainIndices = 1:75;
testIndices = 76:100;
% Split the data
trainData = data(trainIndices, :);
testData = data(testIndices, :);
I hope this helps.
  2 个评论
Mark
Mark 2024-9-23
Thanks for you help. Apologies if this next questions is a bit dumb, I'm still getting to grips with Matlab, but how do I then add that in to the code for computing the tree? For example, I use the below code:
MdlA = fitctree(input,output); % for unvalidated trees
MdlB = fitctree(input,output,'CrossVal','on') % for validated trees.
How in the case of MdlB, do I basically tell it to use rows 1-75 for the initial output, but 76-100 for the cross validation step?
Udit06
Udit06 2024-9-23
Ran in:
Hi Mark,
When you use the fitctree function with the 'CrossVal','on' option, MATLAB automatically performs cross-validation by splitting the data into multiple folds. You can find the same on the following MathWorks documentation:
https://www.mathworks.com/help/stats/fitctree.html#:~:text=Train%20a%20classification%20tree%20using%20the%20default%20values%20for%20tree%20depth%20control.%20Cross%2Dvalidate%20the%20model%20by%20using%2010%2Dfold%20cross%2Dvalidation.
However, if you want to manually specify the training and test sets, you should handle the splitting yourself rather than relying on the built-in cross-validation. You can find the code snippet on how to train the model using manually specifying train and test sets:
% Clear workspace
clear;
% Load the ionosphere dataset
load ionosphere;
% Define training data ratio and calculate number of training samples
trainRatio = 0.7;
numTrainSamples = round(trainRatio * size(X, 1));
% Split data into training and test sets
X_train = X(1:numTrainSamples, :);
Y_train = Y(1:numTrainSamples);
X_test = X(numTrainSamples+1:end, :);
Y_test = Y(numTrainSamples+1:end, :);
% Train a decision tree classifier
MdlA = fitctree(X_train, Y_train);
% Visualize the decision tree
view(MdlA, 'Mode', 'graph');
% Predict labels for the test set
Y_pred = predict(MdlA, X_test);
% Convert cell arrays to matrices for comparison
Y_test = cell2mat(Y_test);
Y_pred = cell2mat(Y_pred);
% Calculate and display accuracy
accuracy = sum(Y_test == Y_pred) / length(Y_test);
fprintf('Test Set Accuracy: %.2f%%\n', accuracy * 100);
Test Set Accuracy: 89.52%
I hope this helps.

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