Open yorkerlin opened 8 years ago
For example, we can use different kernels and inference methods.
@karlnapf I think it will be great if we can automatically include examples/cookbooks in Shoung's API doc.
See Examples using sklearn.gaussian_process.GaussianProcessRegressor
in sklearn's GPR
Integrating this is exactly the point of the cookbook. So yes, once GSoC is over, we have many more than now and we can do this.
BTW there already is a notebook that compares the classifiers #3019
@karlnapf this issue is for kernel machine only :). For classification in GP, there are many components to choose. eg, kernels, likelihoods (eg, logistic, probit), inference methods.
Why would that be different from what we have?
Decision boundary may be different even when training accuracy is the same since we use different kernels/likelihoods/inference methods.
@karlnapf for example, Decision boundary in linear SVM is different from the one in RBF SVM.
reference: http://scikit-learn.org/dev/auto_examples/classification/plot_classifier_comparison.html
Maybe we are misunderstanding each other: Such plots are already in that notebook I referenced above. Kernel SVM vs linear SVM vs GP. I don't see a reason to do another comparison for GPs. Why do you wanna do that?
For example, GP using RBF kernel
vs GP using linear kernel
.
reference: http://people.seas.harvard.edu/~dduvenaud/cookbook/
BTW, only GP with RBF
kernel is used in that plots.
Well I think this is independent of GPs. What about a cookbook on covariance functions and kernels in Shogun?
Do you maybe want to put that in a new (polished) version and put in a separate notebook?
Add a notebook/cookbook about http://people.seas.harvard.edu/~dduvenaud/cookbook/ using the Shogun's GP.
@karlnapf reference: http://scikit-learn.org/dev/auto_examples/classification/plot_classifier_comparison.html