Pushing the docs to dev/ for branch: master, commit bbfa2d456d25172c53994f70d58fa36f2d9bf047

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -16,10 +16,12 @@
 find the best two features predicting disease progression after one year from
 the baseline.
 
-Authors: Manoj Kumar <[email protected]>
-         Maria Telenczuk <https://github.com/maikia>
+Authors: `Manoj Kumar <[email protected]>`_,
+`Maria Telenczuk <https://github.com/maikia>`_
+
 License: BSD 3 clause
 """
+
 print(__doc__)
 
 import matplotlib.pyplot as plt
@@ -50,8 +52,8 @@
 # ---------------------------------------------------------
 #
 # To decide on the importance of the features we are going to use LassoCV
-# estimator. The features with the highest absolute coef_ value are considered
-# the most important
+# estimator. The features with the highest absolute `coef_` value are
+# considered the most important
 
 clf = LassoCV().fit(X, y)
 importance = np.abs(clf.coef_)
@@ -63,9 +65,9 @@
 #
 # Now we want to select the two features which are the most important.
 # SelectFromModel() allows for setting the threshold. Only the features with
-# the coef_ higher than the threshold will remain. Here, we want to set the
-# threshold slightly above the third highest coef_ calculated by LassoCV() from
-# our data.
+# the `coef_` higher than the threshold will remain. Here, we want to set the
+# threshold slightly above the third highest `coef_` calculated by LassoCV()
+# from our data.
 
 idx_third = importance.argsort()[-3]
 threshold = importance[idx_third] + 0.01

dev/_downloads/9a690c19be1584e1c965a01a1e733b2b/plot_select_from_model_diabetes.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Feature selection using SelectFromModel and LassoCV\n\n\nUse SelectFromModel meta-transformer along with Lasso to select the best\ncouple of features from the diabetes dataset.\n\nSince the L1 norm promotes sparsity of features we might be interested in\nselecting only a subset of the most interesting features from the dataset. This\nexample shows how to select two the most interesting features from the diabetes\ndataset.\n\nDiabetes dataset consists of 10 variables (features) collected from 442\ndiabetes patients. This example shows how to use SelectFromModel and LassoCv to\nfind the best two features predicting disease progression after one year from\nthe baseline.\n\nAuthors: Manoj Kumar <[email protected]>\n         Maria Telenczuk <https://github.com/maikia>\nLicense: BSD 3 clause\n"
+        "\n# Feature selection using SelectFromModel and LassoCV\n\n\nUse SelectFromModel meta-transformer along with Lasso to select the best\ncouple of features from the diabetes dataset.\n\nSince the L1 norm promotes sparsity of features we might be interested in\nselecting only a subset of the most interesting features from the dataset. This\nexample shows how to select two the most interesting features from the diabetes\ndataset.\n\nDiabetes dataset consists of 10 variables (features) collected from 442\ndiabetes patients. This example shows how to use SelectFromModel and LassoCv to\nfind the best two features predicting disease progression after one year from\nthe baseline.\n\nAuthors: `Manoj Kumar <[email protected]>`_,\n`Maria Telenczuk <https://github.com/maikia>`_\n\nLicense: BSD 3 clause\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Find importance of the features\n---------------------------------------------------------\n\nTo decide on the importance of the features we are going to use LassoCV\nestimator. The features with the highest absolute coef_ value are considered\nthe most important\n\n"
+        "Find importance of the features\n---------------------------------------------------------\n\nTo decide on the importance of the features we are going to use LassoCV\nestimator. The features with the highest absolute `coef_` value are\nconsidered the most important\n\n"
       ]
     },
     {
@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Select from the model features with the higest score\n---------------------------------------------------------\n\nNow we want to select the two features which are the most important.\nSelectFromModel() allows for setting the threshold. Only the features with\nthe coef_ higher than the threshold will remain. Here, we want to set the\nthreshold slightly above the third highest coef_ calculated by LassoCV() from\nour data.\n\n"
+        "Select from the model features with the higest score\n---------------------------------------------------------\n\nNow we want to select the two features which are the most important.\nSelectFromModel() allows for setting the threshold. Only the features with\nthe `coef_` higher than the threshold will remain. Here, we want to set the\nthreshold slightly above the third highest `coef_` calculated by LassoCV()\nfrom our data.\n\n"
       ]
     },
     {