Pushing the docs to dev/ for branch: main, commit 974cd7739879ffa29c93276cbd1e46869d181545

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -36,7 +36,7 @@
 # ------------------------------------
 #
 # To get an idea of the importance of the features, we are going to use the
-# :class:`~sklearn.linear_model.LassoCV` estimator. The features with the
+# :class:`~sklearn.linear_model.RidgeCV` estimator. The features with the
 # highest absolute `coef_` value are considered the most important.
 # We can observe the coefficients directly without needing to scale them (or
 # scale the data) because from the description above, we know that the features
@@ -46,10 +46,10 @@
 # :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.
 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.linear_model import LassoCV
+from sklearn.linear_model import RidgeCV
 
-lasso = LassoCV().fit(X, y)
-importance = np.abs(lasso.coef_)
+ridge = RidgeCV(alphas=np.logspace(-6, 6, num=5)).fit(X, y)
+importance = np.abs(ridge.coef_)
 feature_names = np.array(diabetes.feature_names)
 plt.bar(height=importance, x=feature_names)
 plt.title("Feature importances via coefficients")
@@ -73,7 +73,7 @@
 threshold = np.sort(importance)[-3] + 0.01
 
 tic = time()
-sfm = SelectFromModel(lasso, threshold=threshold).fit(X, y)
+sfm = SelectFromModel(ridge, threshold=threshold).fit(X, y)
 toc = time()
 print(f"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}")
 print(f"Done in {toc - tic:.3f}s")
@@ -98,13 +98,13 @@
 
 tic_fwd = time()
 sfs_forward = SequentialFeatureSelector(
-    lasso, n_features_to_select=2, direction="forward"
+    ridge, n_features_to_select=2, direction="forward"
 ).fit(X, y)
 toc_fwd = time()
 
 tic_bwd = time()
 sfs_backward = SequentialFeatureSelector(
-    lasso, n_features_to_select=2, direction="backward"
+    ridge, n_features_to_select=2, direction="backward"
 ).fit(X, y)
 toc_bwd = time()
 

dev/_downloads/53e76f761ef04e8d06fa5757554513b0/plot_select_from_model_diabetes.py

@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Feature importance from coefficients\n\nTo get an idea of the importance of the features, we are going to use the\n:class:`~sklearn.linear_model.LassoCV` estimator. The features with the\nhighest absolute `coef_` value are considered the most important.\nWe can observe the coefficients directly without needing to scale them (or\nscale the data) because from the description above, we know that the features\nwere already standardized.\nFor a more complete example on the interpretations of the coefficients of\nlinear models, you may refer to\n`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.\n\n"
+        "## Feature importance from coefficients\n\nTo get an idea of the importance of the features, we are going to use the\n:class:`~sklearn.linear_model.RidgeCV` estimator. The features with the\nhighest absolute `coef_` value are considered the most important.\nWe can observe the coefficients directly without needing to scale them (or\nscale the data) because from the description above, we know that the features\nwere already standardized.\nFor a more complete example on the interpretations of the coefficients of\nlinear models, you may refer to\n`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.\n\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import LassoCV\n\nlasso = LassoCV().fit(X, y)\nimportance = np.abs(lasso.coef_)\nfeature_names = np.array(diabetes.feature_names)\nplt.bar(height=importance, x=feature_names)\nplt.title(\"Feature importances via coefficients\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import RidgeCV\n\nridge = RidgeCV(alphas=np.logspace(-6, 6, num=5)).fit(X, y)\nimportance = np.abs(ridge.coef_)\nfeature_names = np.array(diabetes.feature_names)\nplt.bar(height=importance, x=feature_names)\nplt.title(\"Feature importances via coefficients\")\nplt.show()"
       ]
     },
     {
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SelectFromModel\nfrom time import time\n\nthreshold = np.sort(importance)[-3] + 0.01\n\ntic = time()\nsfm = SelectFromModel(lasso, threshold=threshold).fit(X, y)\ntoc = time()\nprint(f\"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}\")\nprint(f\"Done in {toc - tic:.3f}s\")"
+        "from sklearn.feature_selection import SelectFromModel\nfrom time import time\n\nthreshold = np.sort(importance)[-3] + 0.01\n\ntic = time()\nsfm = SelectFromModel(ridge, threshold=threshold).fit(X, y)\ntoc = time()\nprint(f\"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}\")\nprint(f\"Done in {toc - tic:.3f}s\")"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SequentialFeatureSelector\n\ntic_fwd = time()\nsfs_forward = SequentialFeatureSelector(\n    lasso, n_features_to_select=2, direction=\"forward\"\n).fit(X, y)\ntoc_fwd = time()\n\ntic_bwd = time()\nsfs_backward = SequentialFeatureSelector(\n    lasso, n_features_to_select=2, direction=\"backward\"\n).fit(X, y)\ntoc_bwd = time()\n\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs_forward.get_support()]}\"\n)\nprint(f\"Done in {toc_fwd - tic_fwd:.3f}s\")\nprint(\n    \"Features selected by backward sequential selection: \"\n    f\"{feature_names[sfs_backward.get_support()]}\"\n)\nprint(f\"Done in {toc_bwd - tic_bwd:.3f}s\")"
+        "from sklearn.feature_selection import SequentialFeatureSelector\n\ntic_fwd = time()\nsfs_forward = SequentialFeatureSelector(\n    ridge, n_features_to_select=2, direction=\"forward\"\n).fit(X, y)\ntoc_fwd = time()\n\ntic_bwd = time()\nsfs_backward = SequentialFeatureSelector(\n    ridge, n_features_to_select=2, direction=\"backward\"\n).fit(X, y)\ntoc_bwd = time()\n\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs_forward.get_support()]}\"\n)\nprint(f\"Done in {toc_fwd - tic_fwd:.3f}s\")\nprint(\n    \"Features selected by backward sequential selection: \"\n    f\"{feature_names[sfs_backward.get_support()]}\"\n)\nprint(f\"Done in {toc_bwd - tic_bwd:.3f}s\")"
       ]
     },
     {