I have two datasets and I want to train a SVM classification model (fitcsvm) by one of them and then predict labels for the other one. I use 10-fold cross-validation (crossval) to train my model so I have 10 different models. My question is which one of these models are the best for prediction and how can I find that?
here is my code:
Mdl = fitcsvm(trainingData,labels);
CVMdl = crossval(Mdl);
You may have mixed up something here. The function fitcsvm trains a single model and the function crossval validates this single model. It will then return an evaluation value.
In general, you cannot train a model by cross-validation (as it says, it is a validation technique). However, you can use cross-validation to train good models.
What you are looking for is a sort of hyperparameter optimization. Those are methods that automatically train multiple models on a given data set to find the best tuning values for the SVM. Have a look at the docs here
You can turn it on like this
Mdl = fitcsvm(trainingData,labels,'OptimizeHyperparameters','auto')
You may want to use cross-validation to train multiple models with the same tuning parameters but I guess, you'll have to write this yourself. Perhaps this already helps you.
Related
I have a train dataset and a test dataset, and I train a SVM with fitcsvm in MATLAB. Then, I proceed to test the trained model with predict. I'm always using the same datasets, but I keep getting different AUCs for the same model, which makes me wonder where in the process is there a random component. Note that
I'm aware of the fact that formally there isn't such thing as ROC curve or AUC and
I'm not asking for the statistical background of the SVM problem. It is relative to the matlab implementation of the training/test algorithm. I expected to have the same results because the training algorithm is, afaik, a deterministic process.
I am classifying gender using a KNN classifier.
I want to add an SVM classifier instead of KNN classifier with the same labels of 0 and 1 (0 for women and 1 for men)
I have a matrix of test examples, sample, a matrix of training examples, training, and a vector with the labels for the training examples group. I want class, a vector of the labels for the test examples.
class = knnclassify(sample, training, group);
if class==1
x='Male';
else
x='Female';
end
How can I change this code to find class using an SVM?
To train an SVM, you will need the Statistics and Machine Learning Toolbox.
The biggest difference between the knnclassify and using an SVM classifier is that training and classifying new labels will be two separate steps.
1. Train your SVM : fitcsvm
This step teaches the classifier how to distinguish between your two classes. It is learning a linear separator (or a weighted combination of the features) which has the largest margin between positive and negative examples. All the examples you give it need to have ground truth labels.
SVM's have many tunable parameters that you can adjust during the training step. There are several good tutorials in the Matlab documentation which describe the differences, but for the most basic version, you can just use your training examples
model = fitcsvm(training,group);
This model will be used in the next step.
2. Classify new examples : predict
To classify your new example, run
class = predict(sample, model);
Notes:
Using your model, you can also run cross-fold validation, useful for accuracy analysis.
cvModel = crossval(model);
classError = kfoldLoss(cvModel);
You can also save your model, like any other Matlab variable for future use.
save('model.m', 'model');
knnclassify comes from the bioinformatics toolbox. In the Statistics and Machine Learning Toolbox, there is also a KNN model which you train with fitcknn and classify with predict. The benefit is that you can reuse your KNN model with several sets of data, compare cross-validation results, and save it for future use.
I have a dataset which I use for classifcation with libSVM in Matlab. The dataset consists of 4 classes.
For parameter selection of SVM I can do nested cross-validation. The problem is that I also need the value of the best parameters in the end.
After having done the nested cross-validation and having the final accuracy I want the values of the best parameters. Then I will train a SVM for each class (one-vs-all) with the best parameters for selecting the most important features (according to heighest weight), i.e. feature importance map.
How can I do this? Should I just not do nested cross-validation and only looping over all parameters and doing cross-validation?
Second, if I use a linear SVM then using this weight vector w for assigning importance to features works, but does it also work for non-linear SVM (e.g. rbf kernel)?
To find the "best" parameters for your kernel of choice, you have to loop through all parameters to perform a so called "grid search". LIBSVM does not support a build-in grid-search mechanismn.
Regarding your second question, I would suggest to perform a feature selection (e.g. Information Gain, Mutual Information, ...) as a pre-processing step before the actual work with the SVM and in a second step take the weight vector
s into consideration (but I am not sure, if this will work with RBF or Gaußian Kernels...).
I trained a ensemble model (RUSBoost) for a binary classification problem by the function fitensemble() in Matlab 2014a. The training by this function is performed 10-fold cross-validation through the input parameter "kfold" of the function fitensemble().
However, the output model trained by this function cannot be used to predict the labels of new data if I use the predict(model, Xtest). I checked the Matlab documents, which says we can use kfoldPredict() function to evaluate the trained model. But I did not find any input of the new data through this function. Also, I found the structure of the trained model with cross-validation is different from that model without cross-validation. So, could anyone please advise me how to use the model, which is trained with cross-validation, to predict labels of new data? Thanks!
kfoldPredict() needs a RegressionPartitionedModel or ClassificationPartitionedEnsemble object as input. This already contains the models and data for kfold cross validation.
The RegressionPartitionedModel object has a field Trained, in which the trained learners that are used for cross validation are stored.
You can take any of these learners and use it like predict(learner, Xdata).
Edit:
If k is too large, it is possible that there is too little meaningful data in one or more iteration, so the model for that iteration is less accurate.
There are no general rules for k, but k=10 like in the MATLAB default is a good starting point to play around with it.
Maybe this is also interesting for you: https://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation
this question is about LibSVM or SVMs in general.
I wonder if it is possible to categorize Feature-Vectors of different length with the same SVM Model.
Let's say we train the SVM with about 1000 Instances of the following Feature Vector:
[feature1 feature2 feature3 feature4 feature5]
Now I want to predict a test-vector which has the same length of 5.
If the probability I receive is to poor, I now want to check the first subset of my test-vector containing the columns 2-5. So I want to dismiss the 1 feature.
My question now is: Is it possible to tell the SVM only to check the features 2-5 for prediction (e.g. with weights), or do I have to train different SVM Models. One for 5 features, another for 4 features and so on...?
Thanks in advance...
marcus
You can always remove features from your test points by fiddling with the file, but I highly recommend not using such an approach. An SVM model is valid when all features are present. If you are using the linear kernel, simply setting a given feature to 0 will implicitly cause it to be ignored (though you should not do this). When using other kernels, this is very much a no no.
Using a different set of features for predictions than the set you used for training is not a good approach.
I strongly suggest to train a new model for the subset of features you wish to use in prediction.