Hi,
I have a precomputed kernel of size NxN. I am using GridSearchCV to tune C
parameter of SVM with kernel='precomputed' as follows:
C_range =10.**np.arange(-2,9)param_grid =dict(C=C_range)grid
=GridSearchCV(SVC(kernel='precomputed'),param_grid=param_grid,cv=StratifiedKFold(y=data_label,n_folds=10))grid.fit(kernel,data_label)printgrid.best_score_
This works pretty fine, however since I use the full data for prediction (with
grid.predict(kernel)), it overfits (I get precision/recall = 1.0 most of the
times).
So I would like to first split my data to 10 chunks (9 for training, 1 for
testing) with cross-validation, and in each fold, I want to run GridSearch to
tune the C value on the training set, and test on the testing set.
In order to do this, I sliced the kernel matrix into 100x100 and 50x50
submatrices where I run grid.fit() on one of them and grid.predict() on the
other.
But I get the following error:
ValueError:X.shape[1]=50should be equal to 100,the number of features at
training time
I guess training kernel should have the same dimension as testing kernel, but I
don't understand why, because I simply compute np.dot(X, X.T) for 100x100, and
for 50x50, hence the final kernel have different dimensions..
Thanks,
Ev
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