Pushing the docs to dev/ for branch: main, commit cd892f96883396f71cd6135ee3c56c5f619c53c4

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Partial Dependence and Individual Conditional Expectation Plots\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of features of interest, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the set of features of interest must be small (usually, one or two)\nthus they are usually chosen among the most important features.\n\nSimilarly, an individual conditional expectation (ICE) plot [3]_\nshows the dependence between the target function and a feature of interest.\nHowever, unlike partial dependence plots, which show the average effect of the\nfeatures of interest, ICE plots visualize the dependence of the prediction on a\nfeature for each :term:`sample` separately, with one line per sample.\nOnly one feature of interest is supported for ICE plots.\n\nThis example shows how to obtain partial dependence and ICE 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\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.. [3] Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E., Peeking Inside\n       the Black Box: Visualizing Statistical Learning With Plots of\n       Individual Conditional Expectation. (2015) Journal of Computational and\n       Graphical Statistics, 24(1): 44-65 (https://arxiv.org/abs/1309.6392)\n"
+        "\n# Partial Dependence and Individual Conditional Expectation Plots\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of features of interest, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the set of features of interest must be small (usually, one or two)\nthus they are usually chosen among the most important features.\n\nSimilarly, an individual conditional expectation (ICE) plot [3]_\nshows the dependence between the target function and a feature of interest.\nHowever, unlike partial dependence plots, which show the average effect of the\nfeatures of interest, ICE plots visualize the dependence of the prediction on a\nfeature for each :term:`sample` separately, with one line per sample.\nOnly one feature of interest is supported for ICE plots.\n\nThis example shows how to obtain partial dependence and ICE 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\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.. [3] :arxiv:`Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E. (2015).\n       \"Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of\n       Individual Conditional Expectation\". Journal of Computational and\n       Graphical Statistics, 24(1): 44-65 <1309.6392>`\n"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -27,10 +27,10 @@
 .. [2] For classification you can think of it as the regression score before
        the link function.
 
-.. [3] Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E., Peeking Inside
-       the Black Box: Visualizing Statistical Learning With Plots of
-       Individual Conditional Expectation. (2015) Journal of Computational and
-       Graphical Statistics, 24(1): 44-65 (https://arxiv.org/abs/1309.6392)
+.. [3] :arxiv:`Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E. (2015).
+       "Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of
+       Individual Conditional Expectation". Journal of Computational and
+       Graphical Statistics, 24(1): 44-65 <1309.6392>`
 
 """