Pushing the docs to dev/ for branch: main, commit 40f5eb1386b244fc90b218b1d1fab28421d738c1

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Retrieve the data from Internet\n\nThe data is from 2003 - 2008. This is reasonably calm: (not too long ago so\nthat we get high-tech firms, and before the 2008 crash). This kind of\nhistorical data can be obtained from APIs like the\n[data.nasdaq.com](https://data.nasdaq.com/) and\n[alphavantage.co](https://www.alphavantage.co/).\n\n"
+        "## Retrieve the data from Internet\n\nThe data is from 2003 - 2008. This is reasonably calm: not too long ago so\nthat we get high-tech firms, and before the 2008 crash. This kind of\nhistorical data can be obtained from APIs like the\n[data.nasdaq.com](https://data.nasdaq.com/) and\n[alphavantage.co](https://www.alphavantage.co/).\n\n"
       ]
     },
     {
@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Embedding in 2D space\n\nFor visualization purposes, we need to lay out the different symbols on a\n2D canvas. For this we use `manifold` techniques to retrieve 2D\nembedding.\nWe use a dense eigen_solver to achieve reproducibility (arpack is initiated\nwith the random vectors that we don't control). In addition, we use a large\nnumber of neighbors to capture the large-scale structure.\n\n"
+        "## Embedding in 2D space\n\nFor visualization purposes, we need to lay out the different symbols on a\n2D canvas. For this, we use `manifold` techniques to retrieve 2D\nembedding.\nWe use a dense ``eigen_solver`` to achieve reproducibility (arpack is initiated\nwith the random vectors that we do not control). In addition, we use a large\nnumber of neighbors to capture the large-scale structure.\n\n"
       ]
     },
     {
@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Visualization\n\nThe output of the 3 models are combined in a 2D graph where nodes\nrepresents the stocks and edges the:\n\n- cluster labels are used to define the color of the nodes\n- the sparse covariance model is used to display the strength of the edges\n- the 2D embedding is used to position the nodes in the plan\n\nThis example has a fair amount of visualization-related code, as\nvisualization is crucial here to display the graph. One of the challenge\nis to position the labels minimizing overlap. For this we use an\nheuristic based on the direction of the nearest neighbor along each\naxis.\n\n"
+        "## Visualization\n\nThe output of the 3 models are combined in a 2D graph where nodes\nrepresent the stocks and edges the connections (partial correlations):\n\n- cluster labels are used to define the color of the nodes\n- the sparse covariance model is used to display the strength of the edges\n- the 2D embedding is used to position the nodes in the plan\n\nThis example has a fair amount of visualization-related code, as\nvisualization is crucial here to display the graph. One of the challenges\nis to position the labels minimizing overlap. For this, we use an\nheuristic based on the direction of the nearest neighbor along each\naxis.\n\n"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -17,8 +17,8 @@
 # Retrieve the data from Internet
 # -------------------------------
 #
-# The data is from 2003 - 2008. This is reasonably calm: (not too long ago so
-# that we get high-tech firms, and before the 2008 crash). This kind of
+# The data is from 2003 - 2008. This is reasonably calm: not too long ago so
+# that we get high-tech firms, and before the 2008 crash. This kind of
 # historical data can be obtained from APIs like the
 # `data.nasdaq.com <https://data.nasdaq.com/>`_ and
 # `alphavantage.co <https://www.alphavantage.co/>`_.
@@ -158,10 +158,10 @@
 # ---------------------
 #
 # For visualization purposes, we need to lay out the different symbols on a
-# 2D canvas. For this we use :ref:`manifold` techniques to retrieve 2D
+# 2D canvas. For this, we use :ref:`manifold` techniques to retrieve 2D
 # embedding.
-# We use a dense eigen_solver to achieve reproducibility (arpack is initiated
-# with the random vectors that we don't control). In addition, we use a large
+# We use a dense ``eigen_solver`` to achieve reproducibility (arpack is initiated
+# with the random vectors that we do not control). In addition, we use a large
 # number of neighbors to capture the large-scale structure.
 
 # Finding a low-dimension embedding for visualization: find the best position of
@@ -180,15 +180,15 @@
 # -------------
 #
 # The output of the 3 models are combined in a 2D graph where nodes
-# represents the stocks and edges the:
+# represent the stocks and edges the connections (partial correlations):
 #
 # - cluster labels are used to define the color of the nodes
 # - the sparse covariance model is used to display the strength of the edges
 # - the 2D embedding is used to position the nodes in the plan
 #
 # This example has a fair amount of visualization-related code, as
-# visualization is crucial here to display the graph. One of the challenge
-# is to position the labels minimizing overlap. For this we use an
+# visualization is crucial here to display the graph. One of the challenges
+# is to position the labels minimizing overlap. For this, we use an
 # heuristic based on the direction of the nearest neighbor along each
 # axis.