Hi, Loukas and Mahesh,
for LOOCV, you could e.g., use the LeaveOneOut class
```
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import LeaveOneOut
loo = LeaveOneOut()
lda = LinearDiscriminantAnalysis()
test_fold_predictions = []
for train_index, test_index in loo.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
lda.fit(X_train, y_train)
test_fold_predictions.append(lda.predict(X_test))
```
or you could pass the loo to the cross_val_score function directly:
```
from sklearn.model_selection import cross_val_score
cross_val_score(estimator=lda, X=X, y=y, cv=loo)
```
Best,
Sebastian
> On Mar 7, 2017, at 10:01 AM, Serafeim Loukas <[email protected]> wrote:
>
> Dear Mahesh,
>
> Thank you for your response.
>
> I read the documentation however I did not find anything related to
> cross-validation (leave one out).
> Can you give me a hint?
>
> Thank you,
> S
>
> .............................................
> Loukas Serafeim
> University of Geneva
> email: [email protected]
>
>
> 2017-03-07 10:56 GMT+01:00 Mahesh Kulkarni <[email protected]>:
> Yes. Please see following link:
>
> http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
>
> On Tue, Mar 7, 2017 at 3:18 PM, Serafeim Loukas <[email protected]> wrote:
> Dear scikit members,
>
>
> I would like to ask if there is any function that implements Linear
> Discriminant Analysis with Cross Validation (leave one out).
>
> Thank you in advance,
> S
>
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