Pushing the docs to dev/ for branch: main, commit b84d67751753cd7ee81b3ede8d3d16951ddc4cef

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Analysis of the plots\n\nWe can clearly see on the PDPs (thick blue line) that the median house price\nshows a linear relationship with the median income (top left) and that the\nhouse price drops when the average occupants per household increases (top\nmiddle). The top right plot shows that the house age in a district does not\nhave a strong influence on the (median) house price; so does the average\nrooms per household.\n\nThe ICE curves (light blue lines) complement the analysis: we can see that\nthere are some exceptions, where the house price remain constant with median\nincome and average occupants. On the other hand, while the house age (top\nright) does not have a strong influence on the median house price on average,\nthere seems to be a number of exceptions where the house price increase when\nbetween the ages 15-25. Similar exceptions can be observed for the average\nnumber of rooms (bottom left). Therefore, ICE plots show some individual\neffect which are attenuated by taking the averages.\n\nIn all plots, the tick marks on the x-axis represent the deciles of the\nfeature values in the training data.\n\nWe also observe that :class:`~sklearn.neural_network.MLPRegressor` has much\nsmoother predictions than\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor`.\n\nHowever, it is worth noting that we are creating potential meaningless\nsynthetic samples if features are correlated.\n\n"
+        "### Analysis of the plots\n\nWe can clearly see on the PDPs (dashed orange line) that the median house price\nshows a linear relationship with the median income (top left) and that the\nhouse price drops when the average occupants per household increases (top\nmiddle). The top right plot shows that the house age in a district does not\nhave a strong influence on the (median) house price; so does the average\nrooms per household.\n\nThe ICE curves (light blue lines) complement the analysis: we can see that\nthere are some exceptions, where the house price remain constant with median\nincome and average occupants. On the other hand, while the house age (top\nright) does not have a strong influence on the median house price on average,\nthere seems to be a number of exceptions where the house price increase when\nbetween the ages 15-25. Similar exceptions can be observed for the average\nnumber of rooms (bottom left). Therefore, ICE plots show some individual\neffect which are attenuated by taking the averages.\n\nIn all plots, the tick marks on the x-axis represent the deciles of the\nfeature values in the training data.\n\nWe also observe that :class:`~sklearn.neural_network.MLPRegressor` has much\nsmoother predictions than\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor`.\n\nHowever, it is worth noting that we are creating potential meaningless\nsynthetic samples if features are correlated.\n\n"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -191,7 +191,7 @@
 # Analysis of the plots
 # .....................
 #
-# We can clearly see on the PDPs (thick blue line) that the median house price
+# We can clearly see on the PDPs (dashed orange line) that the median house price
 # shows a linear relationship with the median income (top left) and that the
 # house price drops when the average occupants per household increases (top
 # middle). The top right plot shows that the house age in a district does not