How can I Normalize test data by applying the mean and Standard deviation of the training data?

I have spam data (training data and Test Data). This file contains a training set of size 3065 and a test set of size 1536. I standardized the features so that they have zero mean and unit variance i.e. calculated the mean and standard deviation of the training data. Now I have to apply those same parameters to normalize the test data without recalculating the mean and standard deviation of the test data and Im stuck on this part. Can you please help.

Answer 1

[taruv harshita priya]

Matlab has an inbuilt function normalize

For the training data

[Normalized_training_data, c,s]= normalize(training data)

For testing data

Normalized_testing_data= normalize(testing data, 'center', c, 'scale', s)

I hope this helps!!!

Enjoy coding!!!

[Steven Lord]

Note that while the normalize function was introduced prior to release R2021a, the ability to center and scale simultaneously via the 'center' and 'scale' methods and to return the centering and scaling parameters was introduced in that release.

Answer 2

[Star Strider]

Is the zscore function what you are looking for?

Answer 3

[Image Analyst]

Do you mean

testData = (testData - trainingMean) / trainingStdDev;

???