8000 DOC Fix some example refs due to renaming examples (#11214) · scikit-learn/scikit-learn@1c61b8a · GitHub
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DOC Fix some example refs due to renaming examples (#11214)
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‎doc/datasets/twenty_newsgroups.rst

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@@ -135,7 +135,7 @@ which is fast to train and achieves a decent F-score::
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>>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
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0.88213592402729568
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(The example :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py` shuffles
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(The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles
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the training and test data, instead of segmenting by time, and in that case
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multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious
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yet of what's going on inside this classifier?)
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* :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
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* :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`

‎doc/modules/clustering.rst

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* :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of KMeans and
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MiniBatchKMeans
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* :ref:`sphx_glr_auto_examples_text_document_clustering.py`: Document clustering using sparse
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* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering using sparse
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MiniBatchKMeans
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* :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py`

‎doc/modules/decomposition.rst

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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_text_document_clustering.py`
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* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
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.. topic:: References:
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‎doc/modules/feature_extraction.rst

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with fast and scalable linear models to train **document classifiers**,
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for instance:
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* :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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In an **unsupervised setting** it can be used to group similar documents
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together by applying clustering algorithms such as :ref:`k_means`:
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* :ref:`sphx_glr_auto_examples_text_document_clustering.py`
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* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
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Finally it is possible to discover the main topics of a corpus by
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relaxing the hard assignment constraint of clustering, for instance by

‎doc/modules/feature_selection.rst

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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`: Comparison
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* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`: Comparison
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of different algorithms for document classification including L1-based
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feature selection.
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‎doc/modules/linear_model.rst

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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py`
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* :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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Ridge Complexity

‎doc/modules/model_evaluation.rst

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for an example of using a confusion matrix to classify
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hand-written digits.
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* See :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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for an example of using a confusion matrix to classify text
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documents.
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for an example of classification report usage for
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hand-written digits.
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* See :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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for an example of classification report usage for text
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documents.
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.. topic:: Examples:
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* See :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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* See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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for an example of :func:`f1_score` usage to classify text
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documents.
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‎doc/modules/sgd.rst

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.. topic:: Examples:
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- :ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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- :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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Complexity
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==========

‎doc/tutorial/text_analytics/working_with_text_data.rst

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:class:`CountVectorizer`.
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* If you don't have labels, try using
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:ref:`Clustering <sphx_glr_auto_examples_text_document_clustering.py>`
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:ref:`Clustering <sphx_glr_auto_examples_text_plot_document_clustering.py>`
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on your problem.
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* If you have multiple labels per document, e.g categories, have a look

‎doc/whats_new/older_versions.rst

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- new examples using some of the mlcomp datasets:
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``sphx_glr_auto_examples_mlcomp_sparse_document_classification.py`` (since removed) and
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:ref:`sphx_glr_auto_examples_text_document_classification_20newsgroups.py`
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:ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
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- Many more examples. `See here
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<http://scikit-learn.org/stable/auto_examples/index.html>`_

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