Pushing the docs to dev/ for branch: master, commit 00bc681e1045541fdcecfd3b22e33cfce07ffec8

Author: sklearn-ci

dev/_downloads/2108844cb1b17bae9a6b4c0b0fb3b211/plot_isolation_forest.py

@@ -3,7 +3,7 @@
 IsolationForest example
 ==========================================
 
-An example using :class:`sklearn.ensemble.IsolationForest` for anomaly
+An example using :class:`~sklearn.ensemble.IsolationForest` for anomaly
 detection.
 
 The IsolationForest 'isolates' observations by randomly selecting a feature

dev/_downloads/21fba8cadc21699d2b4699b4ccdad10f/plot_beta_divergence.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Beta-divergence loss functions\n\n\nA plot that compares the various Beta-divergence loss functions supported by\nthe Multiplicative-Update ('mu') solver in :class:`sklearn.decomposition.NMF`.\n"
+        "\n# Beta-divergence loss functions\n\n\nA plot that compares the various Beta-divergence loss functions supported by\nthe Multiplicative-Update ('mu') solver in :class:`~sklearn.decomposition.NMF`.\n"
       ]
     },
     {

dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py

@@ -5,7 +5,7 @@
 
 Transform a signal as a sparse combination of Ricker wavelets. This example
 visually compares different sparse coding methods using the
-:class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known
+:class:`~sklearn.decomposition.SparseCoder` estimator. The Ricker (also known
 as Mexican hat or the second derivative of a Gaussian) is not a particularly
 good kernel to represent piecewise constant signals like this one. It can
 therefore be seen how much adding different widths of atoms matters and it

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Make pipeline to preprocess the data\n#############################################################################\n\n Before we can use Ames dataset we still need to do some preprocessing.\n First, the dataset has many missing values. To impute them, we will exchange\n categorical missing values with the new category 'missing' while the\n numerical missing values with the 'mean' of the column. We will also encode\n the categories with either :class:`sklearn.preprocessing.OneHotEncoder\n <sklearn.preprocessing.OneHotEncoder>` or\n :class:`sklearn.preprocessing.OrdinalEncoder\n <sklearn.preprocessing.OrdinalEncoder>` depending for which type of model we\n will use them (linear or non-linear model). To falicitate this preprocessing\n we will make two pipelines.\n You can skip this section if your data is ready to use and does\n not need preprocessing\n\n"
+        "Make pipeline to preprocess the data\n#############################################################################\n\n Before we can use Ames dataset we still need to do some preprocessing.\n First, the dataset has many missing values. To impute them, we will exchange\n categorical missing values with the new category 'missing' while the\n numerical missing values with the 'mean' of the column. We will also encode\n the categories with either :class:`~sklearn.preprocessing.OneHotEncoder\n <sklearn.preprocessing.OneHotEncoder>` or\n :class:`~sklearn.preprocessing.OrdinalEncoder\n <sklearn.preprocessing.OrdinalEncoder>` depending for which type of model we\n will use them (linear or non-linear model). To facilitate this preprocessing\n we will make two pipelines.\n You can skip this section if your data is ready to use and does\n not need preprocessing\n\n"
       ]
     },
     {

dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb

@@ -4,7 +4,7 @@
 ==============================
 
 A plot that compares the various Beta-divergence loss functions supported by
-the Multiplicative-Update ('mu') solver in :class:`sklearn.decomposition.NMF`.
+the Multiplicative-Update ('mu') solver in :class:`~sklearn.decomposition.NMF`.
 """
 import numpy as np
 import matplotlib.pyplot as plt

dev/_downloads/6d09465eed1ee4ede505244049097627/plot_beta_divergence.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Early stopping of Gradient Boosting\n\n\nGradient boosting is an ensembling technique where several weak learners\n(regression trees) are combined to yield a powerful single model, in an\niterative fashion.\n\nEarly stopping support in Gradient Boosting enables us to find the least number\nof iterations which is sufficient to build a model that generalizes well to\nunseen data.\n\nThe concept of early stopping is simple. We specify a ``validation_fraction``\nwhich denotes the fraction of the whole dataset that will be kept aside from\ntraining to assess the validation loss of the model. The gradient boosting\nmodel is trained using the training set and evaluated using the validation set.\nWhen each additional stage of regression tree is added, the validation set is\nused to score the model.  This is continued until the scores of the model in\nthe last ``n_iter_no_change`` stages do not improve by atleast `tol`. After\nthat the model is considered to have converged and further addition of stages\nis \"stopped early\".\n\nThe number of stages of the final model is available at the attribute\n``n_estimators_``.\n\nThis example illustrates how the early stopping can used in the\n:class:`sklearn.ensemble.GradientBoostingClassifier` model to achieve\nalmost the same accuracy as compared to a model built without early stopping\nusing many fewer estimators. This can significantly reduce training time,\nmemory usage and prediction latency.\n"
+        "\n# Early stopping of Gradient Boosting\n\n\nGradient boosting is an ensembling technique where several weak learners\n(regression trees) are combined to yield a powerful single model, in an\niterative fashion.\n\nEarly stopping support in Gradient Boosting enables us to find the least number\nof iterations which is sufficient to build a model that generalizes well to\nunseen data.\n\nThe concept of early stopping is simple. We specify a ``validation_fraction``\nwhich denotes the fraction of the whole dataset that will be kept aside from\ntraining to assess the validation loss of the model. The gradient boosting\nmodel is trained using the training set and evaluated using the validation set.\nWhen each additional stage of regression tree is added, the validation set is\nused to score the model.  This is continued until the scores of the model in\nthe last ``n_iter_no_change`` stages do not improve by atleast `tol`. After\nthat the model is considered to have converged and further addition of stages\nis \"stopped early\".\n\nThe number of stages of the final model is available at the attribute\n``n_estimators_``.\n\nThis example illustrates how the early stopping can used in the\n:class:`~sklearn.ensemble.GradientBoostingClassifier` model to achieve\nalmost the same accuracy as compared to a model built without early stopping\nusing many fewer estimators. This can significantly reduce training time,\nmemory usage and prediction latency.\n"
       ]
     },
     {

dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb

@@ -25,7 +25,7 @@
 ``n_estimators_``.
 
 This example illustrates how the early stopping can used in the
-:class:`sklearn.ensemble.GradientBoostingClassifier` model to achieve
+:class:`~sklearn.ensemble.GradientBoostingClassifier` model to achieve
 almost the same accuracy as compared to a model built without early stopping
 using many fewer estimators. This can significantly reduce training time,
 memory usage and prediction latency.

dev/_downloads/be911e971b87fe80b6899069dbcfb737/plot_gradient_boosting_early_stopping.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Gradient Boosting regression\n\n\nThis example demonstrates Gradient Boosting to produce a predictive\nmodel from an ensemble of weak predictive models. Gradient boosting can be used\nfor regression and classification problems. Here, we will train a model to\ntackle a diabetes regression task. We will obtain the results from\n:class:`~sklearn.ensemble.GradientBoostingRegressor` with least squares loss\nand 500 regression trees of depth 4.\n\nNote: For larger datasets (n_samples >= 10000), please refer to\n:class:`sklearn.ensemble.HistGradientBoostingRegressor`.\n"
+        "\n# Gradient Boosting regression\n\n\nThis example demonstrates Gradient Boosting to produce a predictive\nmodel from an ensemble of weak predictive models. Gradient boosting can be used\nfor regression and classification problems. Here, we will train a model to\ntackle a diabetes regression task. We will obtain the results from\n:class:`~sklearn.ensemble.GradientBoostingRegressor` with least squares loss\nand 500 regression trees of depth 4.\n\nNote: For larger datasets (n_samples >= 10000), please refer to\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor`.\n"
       ]
     },
     {

dev/_downloads/c536eb92f539255e80e2b3ef5200e7a1/plot_gradient_boosting_regression.ipynb

@@ -76,11 +76,11 @@ def load_ames_housing():
 # First, the dataset has many missing values. To impute them, we will exchange
 # categorical missing values with the new category 'missing' while the
 # numerical missing values with the 'mean' of the column. We will also encode
-# the categories with either :class:`sklearn.preprocessing.OneHotEncoder
+# the categories with either :class:`~sklearn.preprocessing.OneHotEncoder
 # <sklearn.preprocessing.OneHotEncoder>` or
-# :class:`sklearn.preprocessing.OrdinalEncoder
+# :class:`~sklearn.preprocessing.OrdinalEncoder
 # <sklearn.preprocessing.OrdinalEncoder>` depending for which type of model we
-# will use them (linear or non-linear model). To falicitate this preprocessing
+# will use them (linear or non-linear model). To facilitate this preprocessing
 # we will make two pipelines.
 # You can skip this section if your data is ready to use and does
 # not need preprocessing