Pushing the docs to dev/ for branch: main, commit 41ffd79139cc883508395a01f6b9cb4c9bc8e312

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -33,7 +33,7 @@
       "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 quandl.com and\nalphavantage.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"
       ]
     },
     {
@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n## Learning a graph structure\n\nWe use sparse inverse covariance estimation to find which quotes are\ncorrelated conditionally on the others. Specifically, sparse inverse\ncovariance gives us a graph, that is a list of connection. For each\nsymbol, the symbols that it is connected too are those useful to explain\nits fluctuations.\n\n"
+        "\n## Learning a graph structure\n\nWe use sparse inverse covariance estimation to find which quotes are\ncorrelated conditionally on the others. Specifically, sparse inverse\ncovariance gives us a graph, that is a list of connections. For each\nsymbol, the symbols that it is connected to are those useful to explain\nits fluctuations.\n\n"
       ]
     },
     {

dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb

@@ -19,8 +19,9 @@
 #
 # 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 quandl.com and
-# alphavantage.co .
+# historical data can be obtained from APIs like the
+# `data.nasdaq.com <https://data.nasdaq.com/>`_ and
+# `alphavantage.co <https://www.alphavantage.co/>`_.
 
 import sys
 import numpy as np
@@ -112,8 +113,8 @@
 #
 # We use sparse inverse covariance estimation to find which quotes are
 # correlated conditionally on the others. Specifically, sparse inverse
-# covariance gives us a graph, that is a list of connection. For each
-# symbol, the symbols that it is connected too are those useful to explain
+# covariance gives us a graph, that is a list of connections. For each
+# symbol, the symbols that it is connected to are those useful to explain
 # its fluctuations.
 
 from sklearn import covariance