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- function [C, sigma] = dataset3Params(X, y, Xval, yval)
- %DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
- %where you select the optimal (C, sigma) learning parameters to use for SVM
- %with RBF kernel
- % [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and
- % sigma. You should complete this function to return the optimal C and
- % sigma based on a cross-validation set.
- %
- % You need to return the following variables correctly.
- C = 1;
- sigma = 0.1;
- % ====================== YOUR CODE HERE ======================
- % Instructions: Fill in this function to return the optimal C and sigma
- % learning parameters found using the cross validation set.
- % You can use svmPredict to predict the labels on the cross
- % validation set. For example,
- % predictions = svmPredict(model, Xval);
- % will return the predictions on the cross validation set.
- %
- % Note: You can compute the prediction error using
- % mean(double(predictions ~= yval))
- %
- % =========================================================================
- end
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