fitcsvm: how can I decide training (and test) data set composition?

2 visualizaciones (últimos 30 días)
Giorgio De Nunzio
Giorgio De Nunzio el 10 de Mayo de 2016
Comentada: Giorgio De Nunzio el 11 de Mayo de 2016
Hi all. Is it possible to "convince" fitcsvm to use a well-defined (not random) subset of the sample vectors for training (leaving the others for testing)? Not simply a random percentage, as set by the "'Holdout', value" pair, but a list of indices (decided by me) to exactly choose the desired samples from the whole dataset. If I could have a percentage equal to 0 in Holdout, it would do, because the machine would be trained on all the input vectors, then I'd use predict on the test subset. This is absolutely necessary for my code, because I must be able to use the same sample subsets for training etc of different classifiers. To be clearer, when using a neural network (by patternnet, in the Neural Network Toolbox), I can decide which sample vectors to use for training, validation, and test, by net.divideFcn = 'divideind', then setting manually the indices to be used for training etc. Thanks. Best regards. Giorgio
  1 comentario
Giorgio De Nunzio
Giorgio De Nunzio el 11 de Mayo de 2016
Replying to myself... I think I was not understanding but perhaps now it is clear.
By training fitcsvm with a simple fitcsvm(x,y) I can train the machine with the whole set of data (everything is used as the training set). The trained machine can then be applied to a new (test) data set by the predict function. This is exactly what I need.
Only if I set an option such as 'CrossVal', 'CVPartition', etc, I get a ClassificationPartitionedModel, with a number of machines trained accordingly. Otherwise, I get a ClassificationSVM classifier.
It was simple...
Bye
Giorgio

Iniciar sesión para comentar.

Respuestas (0)

Etiquetas

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by