From 8ab3d02db17103474d084f88d0391d44e8fc382f Mon Sep 17 00:00:00 2001 From: "Nikolaas N. Oosterhof" Date: Mon, 22 Jul 2019 17:39:16 +0200 Subject: [PATCH] EXC: update --- doc/source/ex_classify_lda.rst | 2 +- doc/source/nmsm2019_intro.rst | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/source/ex_classify_lda.rst b/doc/source/ex_classify_lda.rst index 0e789ed7..f86bc940 100644 --- a/doc/source/ex_classify_lda.rst +++ b/doc/source/ex_classify_lda.rst @@ -53,7 +53,7 @@ A single classification step can be visualized as follows (more advanced cross-v .. figure:: _static/single_classification.png - *Illustration of (single-fold) classification*. A dataset (left) is split in a train dataset (top dataset) and a test set (bottom dataset), which must have no chunks in common. For training, a classifier (indicated by *f*) takes ``.samples`` and ``.sa.targets`` from the train dataset (horizontal arrow into *f*) and predicts, for the ``.samples`` in the test set (horizontal arrow into *f*), the targets of the test set (U-turn arrow). Classification accuracy can be assessed by computing how many samples in the test set were predicted correctly. + *Illustration of (single-fold) classification*. A dataset (left) is split in a train dataset (top dataset) and a test set (bottom dataset), which must have no chunks in common. For training, a classifier (indicated by *f*) takes ``.samples`` and ``.sa.targets`` from the train dataset (horizontal arrow into *f*) and predicts, for the ``.samples`` in the test set (vertical arrow into *f*), the targets of the test set (U-turn arrow). Classification accuracy can be assessed by computing how many samples in the test set were predicted correctly. Single subject, single fold split-half classification +++++++++++++++++++++++++++++++++++++++++++++++++++++ diff --git a/doc/source/nmsm2019_intro.rst b/doc/source/nmsm2019_intro.rst index a23c8bfd..9780f083 100644 --- a/doc/source/nmsm2019_intro.rst +++ b/doc/source/nmsm2019_intro.rst @@ -148,16 +148,16 @@ Monday -------------- --------------------------------------------------------------------------------------------------- 12:30 Lunch break -------------- --------------------------------------------------------------------------------------------------- -14:00 Split-half correlations. :doc:`ex_splithalf_correlations` +14:00 :doc:`ex_dataset_basics` -------------- --------------------------------------------------------------------------------------------------- 15:30 Coffee break -------------- --------------------------------------------------------------------------------------------------- -16:00-17:00 Classification analysis. :doc:`ex_classify_lda`. Optional :doc:`ex_classify_double_dipping` +16:00-17:30 Split-half correlations. :doc:`ex_splithalf_correlations` -------------- --------------------------------------------------------------------------------------------------- 17:40-18:30 Optional: discuss your data models -------------- --------------------------------------------------------------------------------------------------- Tuesday -09:00 Classification with cross-validation. :doc:`ex_nfold_crossvalidation` +09:00 :doc:`ex_classify_lda`, :doc:`ex_nfold_crossvalidation`. Optional :doc:`ex_classify_double_dipping` -------------- --------------------------------------------------------------------------------------------------- 10:30 Coffee break -------------- ---------------------------------------------------------------------------------------------------