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 <seralouk@gmail.com> 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: seralouk@gmail.com

2017-03-07 10:56 GMT+01:00 Mahesh Kulkarni <maheshak04@gmail.com>: Yes. Please see following link:

http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analys...

On Tue, Mar 7, 2017 at 3:18 PM, Serafeim Loukas <seralouk@gmail.com> 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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Dear Sebastian, Thank you for your response. Best, S ............................................. Loukas Serafeim University of Geneva email: seralouk@gmail.com 2017-03-07 17:56 GMT+01:00 Sebastian Raschka <se.raschka@gmail.com>:

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 <seralouk@gmail.com> 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: seralouk@gmail.com

2017-03-07 10:56 GMT+01:00 Mahesh Kulkarni <maheshak04@gmail.com>: 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 <seralouk@gmail.com> 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

_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

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