Pushing the docs to dev/ for branch: master, commit e9077056d23929a795bccd8f9e03b789845df484

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -5,16 +5,15 @@
 =========================================================
 Linear Regression Example
 =========================================================
-This example uses the only the first feature of the `diabetes` dataset, in
-order to illustrate a two-dimensional plot of this regression technique. The
-straight line can be seen in the plot, showing how linear regression attempts
-to draw a straight line that will best minimize the residual sum of squares
-between the observed responses in the dataset, and the responses predicted by
-the linear approximation.
-
-The coefficients, the residual sum of squares and the coefficient
-of determination are also calculated.
-
+The example below uses only the first feature of the `diabetes` dataset,
+in order to illustrate the data points within the two-dimensional plot.
+The straight line can be seen in the plot, showing how linear regression
+attempts to draw a straight line that will best minimize the
+residual sum of squares between the observed responses in the dataset,
+and the responses predicted by the linear approximation.
+
+The coefficients, residual sum of squares and the coefficient of
+determination are also calculated.
 """
 print(__doc__)
 

dev/_downloads/5ff7ffaf8076af51ffb8c5732f697c8e/plot_ols.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Linear Regression Example\nThis example uses the only the first feature of the `diabetes` dataset, in\norder to illustrate a two-dimensional plot of this regression technique. The\nstraight line can be seen in the plot, showing how linear regression attempts\nto draw a straight line that will best minimize the residual sum of squares\nbetween the observed responses in the dataset, and the responses predicted by\nthe linear approximation.\n\nThe coefficients, the residual sum of squares and the coefficient\nof determination are also calculated.\n"
+        "\n# Linear Regression Example\nThe example below uses only the first feature of the `diabetes` dataset,\nin order to illustrate the data points within the two-dimensional plot.\nThe straight line can be seen in the plot, showing how linear regression\nattempts to draw a straight line that will best minimize the\nresidual sum of squares between the observed responses in the dataset,\nand the responses predicted by the linear approximation.\n\nThe coefficients, residual sum of squares and the coefficient of\ndetermination are also calculated.\n"
       ]
     },
     {