Pushing the docs to dev/ for branch: master, commit 14f5302b7000e9096de93beef37dcdb08f55f128

Author: sklearn-ci

dev/_downloads/0469b1db532e2049dcabff76dcfa3407/plot_cv_indices.py

@@ -103,7 +103,8 @@ def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):
 
 
 ###############################################################################
-# Let's see how it looks for the `KFold` cross-validation object:
+# Let's see how it looks for the :class:`~sklearn.model_selection.KFold`
+# cross-validation object:
 
 fig, ax = plt.subplots()
 cv = KFold(n_splits)

dev/_downloads/0f2070eb0ba0c1cd77d1ae6069402bea/plot_random_multilabel_dataset.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot randomly generated multilabel dataset\n\n\nThis illustrates the `datasets.make_multilabel_classification` dataset\ngenerator. Each sample consists of counts of two features (up to 50 in\ntotal), which are differently distributed in each of two classes.\n\nPoints are labeled as follows, where Y means the class is present:\n\n    =====  =====  =====  ======\n      1      2      3    Color\n    =====  =====  =====  ======\n      Y      N      N    Red\n      N      Y      N    Blue\n      N      N      Y    Yellow\n      Y      Y      N    Purple\n      Y      N      Y    Orange\n      Y      Y      N    Green\n      Y      Y      Y    Brown\n    =====  =====  =====  ======\n\nA star marks the expected sample for each class; its size reflects the\nprobability of selecting that class label.\n\nThe left and right examples highlight the ``n_labels`` parameter:\nmore of the samples in the right plot have 2 or 3 labels.\n\nNote that this two-dimensional example is very degenerate:\ngenerally the number of features would be much greater than the\n\"document length\", while here we have much larger documents than vocabulary.\nSimilarly, with ``n_classes > n_features``, it is much less likely that a\nfeature distinguishes a particular class.\n"
+        "\n# Plot randomly generated multilabel dataset\n\n\nThis illustrates the :func:`~sklearn.datasets.make_multilabel_classification`\ndataset generator. Each sample consists of counts of two features (up to 50 in\ntotal), which are differently distributed in each of two classes.\n\nPoints are labeled as follows, where Y means the class is present:\n\n    =====  =====  =====  ======\n      1      2      3    Color\n    =====  =====  =====  ======\n      Y      N      N    Red\n      N      Y      N    Blue\n      N      N      Y    Yellow\n      Y      Y      N    Purple\n      Y      N      Y    Orange\n      Y      Y      N    Green\n      Y      Y      Y    Brown\n    =====  =====  =====  ======\n\nA star marks the expected sample for each class; its size reflects the\nprobability of selecting that class label.\n\nThe left and right examples highlight the ``n_labels`` parameter:\nmore of the samples in the right plot have 2 or 3 labels.\n\nNote that this two-dimensional example is very degenerate:\ngenerally the number of features would be much greater than the\n\"document length\", while here we have much larger documents than vocabulary.\nSimilarly, with ``n_classes > n_features``, it is much less likely that a\nfeature distinguishes a particular class.\n"
       ]
     },
     {

dev/_downloads/18e2721d4cbbd390f886d71f471ce223/plot_species_distribution_modeling.py

@@ -9,9 +9,8 @@
 mammals given past observations and 14 environmental
 variables. Since we have only positive examples (there are
 no unsuccessful observations), we cast this problem as a
-density estimation problem and use the `OneClassSVM` provided
-by the package `sklearn.svm` as our modeling tool.
-The dataset is provided by Phillips et. al. (2006).
+density estimation problem and use the :class:`sklearn.svm.OneClassSVM`
+as our modeling tool. The dataset is provided by Phillips et. al. (2006).
 If available, the example uses
 `basemap <https://matplotlib.org/basemap/>`_
 to plot the coast lines and national boundaries of South America.

dev/_downloads/2ae02325ffd71a3699f433ae3baecd85/plot_cv_predict.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plotting Cross-Validated Predictions\n\n\nThis example shows how to use `cross_val_predict` to visualize prediction\nerrors.\n"
+        "\n# Plotting Cross-Validated Predictions\n\n\nThis example shows how to use\n:func:`~sklearn.model_selection.cross_val_predict` to visualize prediction\nerrors.\n"
       ]
     },
     {

dev/_downloads/336608b7fc391cd88b2587817f48ffdd/plot_cv_predict.py

@@ -3,7 +3,8 @@
 Plotting Cross-Validated Predictions
 ====================================
 
-This example shows how to use `cross_val_predict` to visualize prediction
+This example shows how to use
+:func:`~sklearn.model_selection.cross_val_predict` to visualize prediction
 errors.
 
 """

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot class probabilities calculated by the VotingClassifier\n\n\nPlot the class probabilities of the first sample in a toy dataset\npredicted by three different classifiers and averaged by the\n`VotingClassifier`.\n\nFirst, three examplary classifiers are initialized (`LogisticRegression`,\n`GaussianNB`, and `RandomForestClassifier`) and used to initialize a\nsoft-voting `VotingClassifier` with weights `[1, 1, 5]`, which means that\nthe predicted probabilities of the `RandomForestClassifier` count 5 times\nas much as the weights of the other classifiers when the averaged probability\nis calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
+        "\n# Plot class probabilities calculated by the VotingClassifier\n\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three examplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
       ]
     },
     {

dev/_downloads/7e4c1ab4eea03643011a811d08c1ab91/plot_voting_probas.ipynb

@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Let's see how it looks for the `KFold` cross-validation object:\n\n"
+        "Let's see how it looks for the :class:`~sklearn.model_selection.KFold`\ncross-validation object:\n\n"
       ]
     },
     {

dev/_downloads/9d49033f7775e9c8e115aa32c938e827/plot_cv_indices.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot individual and voting regression predictions\n\n\nPlot individual and averaged regression predictions for Boston dataset.\n\nFirst, three exemplary regressors are initialized (`GradientBoostingRegressor`,\n`RandomForestRegressor`, and `LinearRegression`) and used to initialize a\n`VotingRegressor`.\n\nThe red starred dots are the averaged predictions.\n"
+        "\n# Plot individual and voting regression predictions\n\n\n.. currentmodule:: sklearn\n\nPlot individual and averaged regression predictions for Boston dataset.\n\nFirst, three exemplary regressors are initialized\n(:class:`~ensemble.GradientBoostingRegressor`,\n:class:`~ensemble.RandomForestRegressor`, and\n:class:`~linear_model.LinearRegression`) and used to initialize a\n:class:`~ensemble.VotingRegressor`.\n\nThe red starred dots are the averaged predictions.\n"
       ]
     },
     {

dev/_downloads/a9a92784a7617f5a14aa93d32f95dff7/plot_voting_regressor.ipynb

@@ -3,11 +3,15 @@
 Plot individual and voting regression predictions
 =================================================
 
+.. currentmodule:: sklearn
+
 Plot individual and averaged regression predictions for Boston dataset.
 
-First, three exemplary regressors are initialized (`GradientBoostingRegressor`,
-`RandomForestRegressor`, and `LinearRegression`) and used to initialize a
-`VotingRegressor`.
+First, three exemplary regressors are initialized
+(:class:`~ensemble.GradientBoostingRegressor`,
+:class:`~ensemble.RandomForestRegressor`, and
+:class:`~linear_model.LinearRegression`) and used to initialize a
+:class:`~ensemble.VotingRegressor`.
 
 The red starred dots are the averaged predictions.