Pushing the docs to dev/ for branch: main, commit 263181a96f363caf74bb0f5ca7442f2e6107ecd2

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We will also generate some random feature data (i.e., 2200 features),\nuncorrelated with the class labels in the iris dataset.\n\n"
+        "We will also generate some random feature data (i.e., 20 features),\nuncorrelated with the class labels in the iris dataset.\n\n"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\n\nn_uncorrelated_features = 2200\nrng = np.random.RandomState(seed=0)\n# Use same number of samples as in iris and 2200 features\nX_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))"
+        "import numpy as np\n\nn_uncorrelated_features = 20\nrng = np.random.RandomState(seed=0)\n# Use same number of samples as in iris and 20 features\nX_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -27,14 +27,14 @@
 y = iris.target
 
 # %%
-# We will also generate some random feature data (i.e., 2200 features),
+# We will also generate some random feature data (i.e., 20 features),
 # uncorrelated with the class labels in the iris dataset.
 
 import numpy as np
 
-n_uncorrelated_features = 2200
+n_uncorrelated_features = 20
 rng = np.random.RandomState(seed=0)
-# Use same number of samples as in iris and 2200 features
+# Use same number of samples as in iris and 20 features
 X_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))
 
 # %%