Pushing the docs to dev/ for branch: main, commit a256e262efd5ff30edd0ab8985f02e921fbc937c

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: a602018b4c2c14238f7cef80bec03477
+config: f278ed75a530e9418aa5854aa4411597
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Comparison of F-test and mutual information\n\nThis example illustrates the differences between univariate F-test statistics\nand mutual information.\n\nWe consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the\ntarget depends on them as follows:\n\ny = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third features is\ncompletely irrelevant.\n\nThe code below plots the dependency of y against individual x_i and normalized\nvalues of univariate F-tests statistics and mutual information.\n\nAs F-test captures only linear dependency, it rates x_1 as the most\ndiscriminative feature. On the other hand, mutual information can capture any\nkind of dependency between variables and it rates x_2 as the most\ndiscriminative feature, which probably agrees better with our intuitive\nperception for this example. Both methods correctly marks x_3 as irrelevant.\n"
+        "\n# Comparison of F-test and mutual information\n\nThis example illustrates the differences between univariate F-test statistics\nand mutual information.\n\nWe consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the\ntarget depends on them as follows:\n\ny = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third feature is\ncompletely irrelevant.\n\nThe code below plots the dependency of y against individual x_i and normalized\nvalues of univariate F-tests statistics and mutual information.\n\nAs F-test captures only linear dependency, it rates x_1 as the most\ndiscriminative feature. On the other hand, mutual information can capture any\nkind of dependency between variables and it rates x_2 as the most\ndiscriminative feature, which probably agrees better with our intuitive\nperception for this example. Both methods correctly mark x_3 as irrelevant.\n"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -9,7 +9,7 @@
 We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
 target depends on them as follows:
 
-y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third features is
+y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third feature is
 completely irrelevant.
 
 The code below plots the dependency of y against individual x_i and normalized
@@ -19,7 +19,7 @@
 discriminative feature. On the other hand, mutual information can capture any
 kind of dependency between variables and it rates x_2 as the most
 discriminative feature, which probably agrees better with our intuitive
-perception for this example. Both methods correctly marks x_3 as irrelevant.
+perception for this example. Both methods correctly mark x_3 as irrelevant.
 
 """