<div dir="auto">Hi Sebastian,<div dir="auto"><br></div><div dir="auto">Thank you</div></div><div class="gmail_extra"><br><div class="gmail_quote">On 7 Mar 2017 10:28 p.m., "Sebastian Raschka" <<a href="mailto:se.raschka@gmail.com">se.raschka@gmail.com</a>> wrote:<br type="attribution"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi, Loukas and Mahesh,<br>
for LOOCV, you could e.g., use the LeaveOneOut class<br>
<br>
```<br>
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis<br>
from sklearn.model_selection import LeaveOneOut<br>
<br>
loo = LeaveOneOut()<br>
lda = LinearDiscriminantAnalysis()<br>
<br>
test_fold_predictions = []<br>
<br>
for train_index, test_index in loo.split(X):<br>
    X_train, X_test = X[train_index], X[test_index]<br>
    y_train, y_test = y[train_index], y[test_index]<br>
    lda.fit(X_train, y_train)<br>
    test_fold_predictions.append(<wbr>lda.predict(X_test))<br>
```<br>
<br>
or you could pass the loo to the cross_val_score function directly:<br>
<br>
```<br>
from sklearn.model_selection import cross_val_score<br>
cross_val_score(estimator=lda, X=X, y=y, cv=loo)<br>
```<br>
<br>
<br>
Best,<br>
Sebastian<br>
<br>
<br>
> On Mar 7, 2017, at 10:01 AM, Serafeim Loukas <<a href="mailto:seralouk@gmail.com">seralouk@gmail.com</a>> wrote:<br>
><br>
> Dear Mahesh,<br>
><br>
> Thank you for your response.<br>
><br>
> I read the documentation however I did not find anything related to cross-validation (leave one out).<br>
> Can you give me a hint?<br>
><br>
> Thank you,<br>
> S<br>
><br>
> ..............................<wbr>...............<br>
> Loukas Serafeim<br>
> University of Geneva<br>
> email: <a href="mailto:seralouk@gmail.com">seralouk@gmail.com</a><br>
><br>
><br>
> 2017-03-07 10:56 GMT+01:00 Mahesh Kulkarni <<a href="mailto:maheshak04@gmail.com">maheshak04@gmail.com</a>>:<br>
> Yes. Please see following link:<br>
><br>
> <a href="http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html" rel="noreferrer" target="_blank">http://scikit-learn.org/<wbr>stable/modules/generated/<wbr>sklearn.discriminant_analysis.<wbr>LinearDiscriminantAnalysis.<wbr>html</a><br>
><br>
> On Tue, Mar 7, 2017 at 3:18 PM, Serafeim Loukas <<a href="mailto:seralouk@gmail.com">seralouk@gmail.com</a>> wrote:<br>
> Dear scikit members,<br>
><br>
><br>
> I would like to ask if there is any function that implements Linear Discriminant Analysis with Cross Validation (leave one out).<br>
><br>
> Thank you in advance,<br>
> S<br>
><br>
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><br>
><br>
><br>
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</blockquote></div></div>