Pushing the docs to dev/ for branch: master, commit 63cd6004bc4f31222ac0b17b47691718123a4e22

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the target feature set must be small (usually, one or two) thus the\ntarget features are usually chosen among the most important features.\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.neural_network.MLPRegressor` and a\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the\nCalifornia housing dataset. The example is taken from [1]_.\n\nThe plots show four 1-way and two 1-way partial dependence plots (ommitted for\n:class:`~sklearn.neural_network.MLPRegressor` due to computation time). The\ntarget variables for the one-way PDP are: median income (`MedInc`), average\noccupants per household (`AvgOccup`), median house age (`HouseAge`), and\naverage rooms per household (`AveRooms`).\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman, \"Elements of Statistical\n       Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n"
+        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the target feature set must be small (usually, one or two) thus the\ntarget features are usually chosen among the most important features.\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.neural_network.MLPRegressor` and a\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the\nCalifornia housing dataset. The example is taken from [1]_.\n\nThe plots show four 1-way and two 1-way partial dependence plots (omitted for\n:class:`~sklearn.neural_network.MLPRegressor` due to computation time). The\ntarget variables for the one-way PDP are: median income (`MedInc`), average\noccupants per household (`AvgOccup`), median house age (`HouseAge`), and\naverage rooms per household (`AveRooms`).\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman, \"Elements of Statistical\n       Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n"
       ]
     },
     {

dev/_downloads/5a693c97e821586539ab9d250762742c/plot_partial_dependence.ipynb

@@ -14,7 +14,7 @@
 :class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the
 California housing dataset. The example is taken from [1]_.
 
-The plots show four 1-way and two 1-way partial dependence plots (ommitted for
+The plots show four 1-way and two 1-way partial dependence plots (omitted for
 :class:`~sklearn.neural_network.MLPRegressor` due to computation time). The
 target variables for the one-way PDP are: median income (`MedInc`), average
 occupants per household (`AvgOccup`), median house age (`HouseAge`), and