On 01/06/2015 01:21 PM, Morgan Hoffman wrote:
Hi Andy,
Thanks for your help. Is there something in the scikit-learn
documentation (or any other resource) that explains why the kernel
matrix at test time needs to be the kernel between the test data and
the training data? I am quite new to mach
0.7 is really a 0.
Thanks!
Date: Tue, 6 Jan 2015 12:45:06 -0500
From: t3k...@gmail.com
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] Cross validation with a pre-computed
kernel
The kernel matrix at test time needs to be the kernel
I am a bit confused as to why you code doesn't crash on the call to the
scaler.
What is the shape of train_gram_matrix and test_gram_matrix?
On 01/06/2015 12:27 PM, Morgan Hoffman wrote:
Hi,
I am trying to do a k-fold cross validation with a precomputed kernel.
However, I end up with an erro
The kernel matrix at test time needs to be the kernel between the test
data and the training data.
Which I guess is not what get_gram_matrix does.
Why are you applying the MinMaxScaler to the gram matrix? I'm not sure
that makes sense...
Without the scaler you could just do
print(cross_val_sc
Hi,
I am trying to do a k-fold cross validation with a precomputed kernel. However,
I end up with an error message that looks like this:
Traceback (most recent call last): File "kfold_simple_data.py", line 64, in
score = clf.score(test_gram_matrix, test_labels) File
"/usr/local/lib/python2
