swarupsrini
diff --git a/‎.gitignore
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diff --git a/‎README.rst
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diff --git a/‎benchmarks/bench_hist_gradient_boosting.py
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diff --git a/‎benchmarks/bench_hist_gradient_boosting_threading.py
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diff --git a/‎build_tools/github/upload_anaconda.sh
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diff --git a/‎build_tools/travis/after_success.sh
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diff --git a/‎doc/developers/develop.rst
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diff --git a/‎doc/developers/utilities.rst
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diff --git a/‎doc/modules/classes.rst
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diff --git a/‎doc/modules/cross_validation.rst
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@@ -80,5 +80,4 @@ sklearn/utils/_seq_dataset.pyx
 sklearn/utils/_seq_dataset.pxd
 sklearn/linear_model/_sag_fast.pyx
 
-venv/
-bugs.py
+venv/
@@ -17,8 +17,8 @@
 .. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
 .. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule
 
-.. |PythonVersion| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue
-.. _PythonVersion: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue
+.. |PythonVersion| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue
+.. _PythonVersion: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue
 
 .. |PyPi| image:: https://badge.fury.io/py/scikit-learn.svg
 .. _PyPi: https://badge.fury.io/py/scikit-learn
 
@@ -110,7 +110,7 @@ def one_run(n_samples):
     else:
         # regression
         if loss == 'default':
-            loss = 'squared_error'
+            loss = 'least_squares'
     est.set_params(loss=loss)
     est.fit(X_train, y_train, sample_weight=sample_weight_train)
     sklearn_fit_duration = time() - tic
 
@@ -112,7 +112,7 @@ def get_estimator_and_data():
 else:
     # regression
     if loss == 'default':
-        loss = 'squared_error'
+        loss = 'least_squares'
 sklearn_est.set_params(loss=loss)
 
 
 
@@ -11,11 +11,7 @@ else
     ANACONDA_TOKEN="$SCIKIT_LEARN_STAGING_UPLOAD_TOKEN"
 fi
 
-# Install Python 3.8 because of a bug with Python 3.9
-export PATH=$CONDA/bin:$PATH
-conda create -n upload -y python=3.8
-source activate upload
-conda install -y anaconda-client
+pip install git+https://github.com/Anaconda-Server/anaconda-client
 
 # Force a replacement if the remote file already exists
 anaconda -t $ANACONDA_TOKEN upload --force -u $ANACONDA_ORG dist/artifact/*
 
@@ -18,16 +18,7 @@ if [[ $BUILD_WHEEL == true && $TRAVIS_EVENT_TYPE != pull_request ]]; then
         ANACONDA_TOKEN="$SCIKIT_LEARN_STAGING_UPLOAD_TOKEN"
     fi
 
-    MINICONDA_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh"
-    wget $MINICONDA_URL -O miniconda.sh
-    MINICONDA_PATH=$HOME/miniconda
-    chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
-
-    # Install Python 3.8 because of a bug with Python 3.9
-    export PATH=$MINICONDA_PATH/bin:$PATH
-    conda create -n upload -y python=3.8
-    source activate upload
-    conda install -y anaconda-client
+    pip install git+https://github.com/Anaconda-Server/anaconda-client
 
     # Force a replacement if the remote file already exists
     anaconda -t $ANACONDA_TOKEN upload --force -u $ANACONDA_ORG wheelhouse/*.whl
 
@@ -641,7 +641,7 @@ In addition to the tags, estimators also need to declare any non-optional
 parameters to ``__init__`` in the ``_required_parameters`` class attribute,
 which is a list or tuple.  If ``_required_parameters`` is only
 ``["estimator"]`` or ``["base_estimator"]``, then the estimator will be
-instantiated with an instance of ``LogisticRegression`` (or
+instantiated with an instance of ``LinearDiscriminantAnalysis`` (or
 ``RidgeRegression`` if the estimator is a regressor) in the tests. The choice
 of these two models is somewhat idiosyncratic but both should provide robust
 closed-form solutions.
 
@@ -196,9 +196,6 @@ Helper Functions
   to ``n``.  Used in :func:`~sklearn.decomposition.dict_learning` and
   :func:`~sklearn.cluster.k_means`.
 
-- :class:`gen_batches`: generator to create slices containing batch size elements 
-  from 0 to ``n``
-
 - :func:`safe_mask`: Helper function to convert a mask to the format expected
   by the numpy array or scipy sparse matrix on which to use it (sparse
   matrices support integer indices only while numpy arrays support both
 
@@ -1176,7 +1176,6 @@ Splitter Classes
    model_selection.ShuffleSplit
    model_selection.StratifiedKFold
    model_selection.StratifiedShuffleSplit
-   model_selection.StratifiedGroupKFold
    model_selection.TimeSeriesSplit
 
 Splitter Functions
@@ -1596,7 +1595,6 @@ Plotting
    utils.extmath.fast_logdet
    utils.extmath.density
    utils.extmath.weighted_mode
-   utils.gen_batches
    utils.gen_even_slices
    utils.graph.single_source_shortest_path_length
    utils.graph_shortest_path.graph_shortest_path
 
@@ -353,7 +353,7 @@ Example of 2-fold cross-validation on a dataset with 4 samples::
 Here is a visualization of the cross-validation behavior. Note that
 :class:`KFold` is not affected by classes or groups.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_006.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_004.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%
@@ -509,7 +509,7 @@ Here is a usage example::
 Here is a visualization of the cross-validation behavior. Note that
 :class:`ShuffleSplit` is not affected by classes or groups.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_008.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_006.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%
@@ -566,7 +566,7 @@ We can see that :class:`StratifiedKFold` preserves the class ratios
 
 Here is a visualization of the cross-validation behavior.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_009.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_007.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%
@@ -585,7 +585,7 @@ percentage for each target class as in the complete set.
 
 Here is a visualization of the cross-validation behavior.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_012.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_009.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%
@@ -645,58 +645,6 @@ size due to the imbalance in the data.
 
 Here is a visualization of the cross-validation behavior.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_007.png
-   :target: ../auto_examples/model_selection/plot_cv_indices.html
-   :align: center
-   :scale: 75%
-
-.. _stratified_group_k_fold:
-
-StratifiedGroupKFold
-^^^^^^^^^^^^^^^^^^^^
-
-:class:`StratifiedGroupKFold` is a cross-validation scheme that combines both
-:class:`StratifiedKFold` and :class:`GroupKFold`. The idea is to try to
-preserve the distribution of classes in each split while keeping each group
-within a single split. That might be useful when you have an unbalanced
-dataset so that using just :class:`GroupKFold` might produce skewed splits.
-
-Example::
-
-  >>> from sklearn.model_selection import StratifiedGroupKFold
-  >>> X = list(range(18))
-  >>> y = [1] * 6 + [0] * 12
-  >>> groups = [1, 2, 3, 3, 4, 4, 1, 1, 2, 2, 3, 4, 5, 5, 5, 6, 6, 6]
-  >>> sgkf = StratifiedGroupKFold(n_splits=3)
-  >>> for train, test in sgkf.split(X, y, groups=groups):
-  ...     print("%s %s" % (train, test))
-  [ 0  2  3  4  5  6  7 10 11 15 16 17] [ 1  8  9 12 13 14]
-  [ 0  1  4  5  6  7  8  9 11 12 13 14] [ 2  3 10 15 16 17]
-  [ 1  2  3  8  9 10 12 13 14 15 16 17] [ 0  4  5  6  7 11]
-
-Implementation notes:
-
-- With the current implementation full shuffle is not possible in most
-  scenarios. When shuffle=True, the following happens:
-
-  1. All groups a shuffled.
-  2. Groups are sorted by standard deviation of classes using stable sort.
-  3. Sorted groups are iterated over and assigned to folds.
-
-  That means that only groups with the same standard deviation of class
-  distribution will be shuffled, which might be useful when each group has only
-  a single class.
-- The algorithm greedily assigns each group to one of n_splits test sets,
-  choosing the test set that minimises the variance in class distribution
-  across test sets. Group assignment proceeds from groups with highest to
-  lowest variance in class frequency, i.e. large groups peaked on one or few
-  classes are assigned first.
-- This split is suboptimal in a sense that it might produce imbalanced splits
-  even if perfect stratification is possible. If you have relatively close
-  distribution of classes in each group, using :class:`GroupKFold` is better.
-
-Here is a visualization of cross-validation behavior for uneven groups:
-
 .. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_005.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
@@ -785,7 +733,7 @@ Here is a usage example::
 
 Here is a visualization of the cross-validation behavior.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_011.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_008.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%
@@ -887,7 +835,7 @@ Example of 3-split time series cross-validation on a dataset with 6 samples::
 
 Here is a visualization of the cross-validation behavior.
 
-.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_013.png
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cv_indices_010.png
    :target: ../auto_examples/model_selection/plot_cv_indices.html
    :align: center
    :scale: 75%