# Classifying data using machine learning

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Dhruv Birla il 14 Lug 2021
Commentato: Dhruv Birla il 21 Lug 2021
Using the fisheriris dataset in MATLAB, I want to use the first 30 datasets of each species for training and then predict the species of the other 20 based on the training data. I tried using the predict function, but it requires the training data vector and the prediction data vector to have the same dimensions. Is there a different function I can use that works the same way as the predict function and allows me to input vectors of varying sizes for training and prediction?
Here is the code I used:
N = size(meas,1);
newLabels = cell(90,1);
newLabels(1:30,1) = species(1:30,1);
newLabels(31:60,1) = species(51:80,1);
newLabels(61:90,1) = species(101:130,1);
trainData = cell(90,2);
trainData = str2double(trainData);
trainData(1:30,1) = meas(1:30,1);
trainData(31:60,1) = meas(51:80,1);
trainData(61:90,1) = meas(101:130,1);
trainData(1:30,2) = meas(1:30,2);
trainData(31:60,2) = meas(51:80,2);
trainData(61:90,2) = meas(101:130,2);
toPredict = cell(90,2);
toPredict = str2double(toPredict);
toPredict(1:30,1) = meas(21:50,1);
toPredict(31:60,1) = meas(71:100,1);
toPredict(61:90,1) = meas(121:150,1);
toPredict(1:30,2) = meas(21:50,2);
toPredict(31:60,2) = meas(71:100,2);
toPredict(61:90,2) = meas(121:150,2);
lda = fitcdiscr(trainData(:,1:2),newLabels);
ldaClass = predict(lda,toPredict);
ldaResubErr = resubLoss(lda);
figure
ldaResubCM = confusionchart(newLabels,ldaClass);
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### Risposta accettata

Hrishikesh Borate il 21 Lug 2021
Hi,
The following code uses the fisheriris dataset, where the first 30 instances of each class are used for training and the next 20 instances of each class are used for prediction.
N = size(meas,1);
newLabels = cell(90,1);
newLabels(1:30,1) = species(1:30,1);
newLabels(31:60,1) = species(51:80,1);
newLabels(61:90,1) = species(101:130,1);
trainData = cell(90,2);
trainData = str2double(trainData);
trainData(1:30,:) = meas(1:30,1:2);
trainData(31:60,:) = meas(51:80,1:2);
trainData(61:90,:) = meas(101:130,1:2);
toPredict = cell(60,2);
toPredict = str2double(toPredict);
toPredict(1:20,:) = meas(31:50,1:2);
toPredict(21:40,:) = meas(81:100,1:2);
toPredict(41:60,:) = meas(131:150,1:2);
toPredictLabels = cell(60,1);
toPredictLabels(1:20,1) = species(31:50,1);
toPredictLabels(21:40,1) = species(81:100,1);
toPredictLabels(41:60,1) = species(131:150,1);
lda = fitcdiscr(trainData(:,1:2),newLabels);
ldaClass = predict(lda,toPredict);
ldaResubErr = resubLoss(lda);
figure
ldaResubCM = confusionchart(toPredictLabels,ldaClass);
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Dhruv Birla il 21 Lug 2021
This works perfectly! Thank you so much!

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