Pushing the docs to dev/ for branch: master, commit a2d361bf40dbe7eee77a8c27aba779358ff96d0e

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot individual and voting regression predictions\n\n\n.. currentmodule:: sklearn\n\nA voting regressor is an ensemble meta-estimator that fits base regressors each\non the whole dataset. It, then, averages the individual predictions to form a\nfinal prediction.\nWe will use three different regressors to predict the data:\n:class:`~ensemble.GradientBoostingRegressor`,\n:class:`~ensemble.RandomForestRegressor`, and\n:class:`~linear_model.LinearRegression`).\nThen, using them we will make voting regressor\n:class:`~ensemble.VotingRegressor`.\n\nFinally, we will plot all of them for comparison.\n\nWe will work with the diabetes dataset which consists of the 10 features\ncollected from a cohort of diabetes patients. The target is the disease\nprogression after one year from the baseline.\n"
+        "\n# Plot individual and voting regression predictions\n\n\n.. currentmodule:: sklearn\n\nA voting regressor is an ensemble meta-estimator that fits several base\nregressors, each on the whole dataset. Then it averages the individual\npredictions to form a final prediction.\nWe will use three different regressors to predict the data:\n:class:`~ensemble.GradientBoostingRegressor`,\n:class:`~ensemble.RandomForestRegressor`, and\n:class:`~linear_model.LinearRegression`).\nThen the above 3 regressors will be used for the\n:class:`~ensemble.VotingRegressor`.\n\nFinally, we will plot the predictions made by all models for comparison.\n\nWe will work with the diabetes dataset which consists of 10 features\ncollected from a cohort of diabetes patients. The target is a quantitative\nmeasure of disease progression one year after baseline.\n"
       ]
     },
     {
@@ -26,14 +26,14 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.ensemble import VotingRegressor"
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.ensemble import VotingRegressor"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Training classifiers\n--------------------------------\n\nFirst, we are going to load diabetes dataset and initiate gradient boosting\nregressor, random forest regressor and linear regression. Next, we are going\nto use each of them to build the voting regressor:\n\n"
+        "Training classifiers\n--------------------------------\n\nFirst, we will load the diabetes dataset and initiate a gradient boosting\nregressor, a random forest regressor and a linear regression. Next, we will\nuse the 3 regressors to build the voting regressor:\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "X, y = datasets.load_diabetes(return_X_y=True)\n\n# Train classifiers\nreg1 = GradientBoostingRegressor(random_state=1)\nreg2 = RandomForestRegressor(random_state=1)\nreg3 = LinearRegression()\n\nreg1.fit(X, y)\nreg2.fit(X, y)\nreg3.fit(X, y)\n\nereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])\nereg.fit(X, y)"
+        "X, y = load_diabetes(return_X_y=True)\n\n# Train classifiers\nreg1 = GradientBoostingRegressor(random_state=1)\nreg2 = RandomForestRegressor(random_state=1)\nreg3 = LinearRegression()\n\nreg1.fit(X, y)\nreg2.fit(X, y)\nreg3.fit(X, y)\n\nereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])\nereg.fit(X, y)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Making predictions\n--------------------------------\n\nNow we will use each of the regressors to make 20 first predictions about the\ndiabetes dataset.\n\n"
+        "Making predictions\n--------------------------------\n\nNow we will use each of the regressors to make the 20 first predictions.\n\n"
       ]
     },
     {
@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Plot the results\n--------------------------------\n\nFinally, we will visualize the 20 predictions. The red stars show the average\nprediction\n\n"
+        "Plot the results\n--------------------------------\n\nFinally, we will visualize the 20 predictions. The red stars show the average\nprediction made by :class:`~ensemble.VotingRegressor`.\n\n"
       ]
     },
     {

dev/_downloads/a9a92784a7617f5a14aa93d32f95dff7/plot_voting_regressor.ipynb

@@ -5,28 +5,28 @@
 
 .. currentmodule:: sklearn
 
-A voting regressor is an ensemble meta-estimator that fits base regressors each
-on the whole dataset. It, then, averages the individual predictions to form a
-final prediction.
+A voting regressor is an ensemble meta-estimator that fits several base
+regressors, each on the whole dataset. Then it averages the individual
+predictions to form a final prediction.
 We will use three different regressors to predict the data:
 :class:`~ensemble.GradientBoostingRegressor`,
 :class:`~ensemble.RandomForestRegressor`, and
 :class:`~linear_model.LinearRegression`).
-Then, using them we will make voting regressor
+Then the above 3 regressors will be used for the
 :class:`~ensemble.VotingRegressor`.
 
-Finally, we will plot all of them for comparison.
+Finally, we will plot the predictions made by all models for comparison.
 
-We will work with the diabetes dataset which consists of the 10 features
-collected from a cohort of diabetes patients. The target is the disease
-progression after one year from the baseline.
+We will work with the diabetes dataset which consists of 10 features
+collected from a cohort of diabetes patients. The target is a quantitative
+measure of disease progression one year after baseline.
 
 """
 print(__doc__)
 
 import matplotlib.pyplot as plt
 
-from sklearn import datasets
+from sklearn.datasets import load_diabetes
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.linear_model import LinearRegression
@@ -36,11 +36,11 @@
 # Training classifiers
 # --------------------------------
 #
-# First, we are going to load diabetes dataset and initiate gradient boosting
-# regressor, random forest regressor and linear regression. Next, we are going
-# to use each of them to build the voting regressor:
+# First, we will load the diabetes dataset and initiate a gradient boosting
+# regressor, a random forest regressor and a linear regression. Next, we will
+# use the 3 regressors to build the voting regressor:
 
-X, y = datasets.load_diabetes(return_X_y=True)
+X, y = load_diabetes(return_X_y=True)
 
 # Train classifiers
 reg1 = GradientBoostingRegressor(random_state=1)
@@ -58,8 +58,7 @@
 # Making predictions
 # --------------------------------
 #
-# Now we will use each of the regressors to make 20 first predictions about the
-# diabetes dataset.
+# Now we will use each of the regressors to make the 20 first predictions.
 
 xt = X[:20]
 
@@ -73,7 +72,7 @@
 # --------------------------------
 #
 # Finally, we will visualize the 20 predictions. The red stars show the average
-# prediction
+# prediction made by :class:`~ensemble.VotingRegressor`.
 
 plt.figure()
 plt.plot(pred1, 'gd', label='GradientBoostingRegressor')