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diff --git a/‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
Lines changed: 17 additions & 17 deletions b/‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
Lines changed: 30 additions & 30 deletions b/‎dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
Lines changed: 30 additions & 30 deletions
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Linear Regression Example\n\nThis example uses the only the first feature of the `diabetes` dataset, in\norder to illustrate a two-dimensional plot of this regression technique. The\nstraight line can be seen in the plot, showing how linear regression attempts\nto draw a straight line that will best minimize the residual sum of squares\nbetween the observed responses in the dataset, and the responses predicted by\nthe linear approximation.\n\nThe coefficients, the residual sum of squares and the variance score are also\ncalculated.\n"
+        "\n# Linear Regression Example\n\nThis example uses the only the first feature of the `diabetes` dataset, in\norder to illustrate a two-dimensional plot of this regression technique. The\nstraight line can be seen in the plot, showing how linear regression attempts\nto draw a straight line that will best minimize the residual sum of squares\nbetween the observed responses in the dataset, and the responses predicted by\nthe linear approximation.\n\nThe coefficients, the residual sum of squares and the coefficient\nof determination are also calculated.\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# Make predictions using the testing set\ndiabetes_y_pred = regr.predict(diabetes_X_test)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint(\"Mean squared error: %.2f\"\n      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\nplt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# Make predictions using the testing set\ndiabetes_y_pred = regr.predict(diabetes_X_test)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint('Mean squared error: %.2f'\n      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# The coefficient of determination: 1 is perfect prediction\nprint('Coefficient of determination: %.2f'\n      % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\nplt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
       ]
     }
   ],
 
@@ -12,8 +12,8 @@
 between the observed responses in the dataset, and the responses predicted by
 the linear approximation.
 
-The coefficients, the residual sum of squares and the variance score are also
-calculated.
+The coefficients, the residual sum of squares and the coefficient
+of determination are also calculated.
 
 """
 print(__doc__)
@@ -55,10 +55,11 @@
 # The coefficients
 print('Coefficients: \n', regr.coef_)
 # The mean squared error
-print("Mean squared error: %.2f"
+print('Mean squared error: %.2f'
       % mean_squared_error(diabetes_y_test, diabetes_y_pred))
-# Explained variance score: 1 is perfect prediction
-print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
+# The coefficient of determination: 1 is perfect prediction
+print('Coefficient of determination: %.2f'
+      % r2_score(diabetes_y_test, diabetes_y_pred))
 
 # Plot outputs
 plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
Original file line number	Diff line number	Diff line change
`@@ -15,7 +15,7 @@`
`15`	`15`	`"cell_type": "markdown",`
`16`	`16`	`"metadata": {},`
`17`	`17`	`"source": [`
`18`		- "\n# Linear Regression Example\n\nThis example uses the only the first feature of the `diabetes` dataset, in\norder to illustrate a two-dimensional plot of this regression technique. The\nstraight line can be seen in the plot, showing how linear regression attempts\nto draw a straight line that will best minimize the residual sum of squares\nbetween the observed responses in the dataset, and the responses predicted by\nthe linear approximation.\n\nThe coefficients, the residual sum of squares and the variance score are also\ncalculated.\n"
	`18`	+ "\n# Linear Regression Example\n\nThis example uses the only the first feature of the `diabetes` dataset, in\norder to illustrate a two-dimensional plot of this regression technique. The\nstraight line can be seen in the plot, showing how linear regression attempts\nto draw a straight line that will best minimize the residual sum of squares\nbetween the observed responses in the dataset, and the responses predicted by\nthe linear approximation.\n\nThe coefficients, the residual sum of squares and the coefficient\nof determination are also calculated.\n"
`19`	`19`	`]`
`20`	`20`	`},`
`21`	`21`	`{`
`@@ -26,7 +26,7 @@`
`26`	`26`	`},`
`27`	`27`	`"outputs": [],`
`28`	`28`	`"source": [`
`29`		- "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# Make predictions using the testing set\ndiabetes_y_pred = regr.predict(diabetes_X_test)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint(\"Mean squared error: %.2f\"\n % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test, color='black')\nplt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
	`29`	+ "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# Make predictions using the testing set\ndiabetes_y_pred = regr.predict(diabetes_X_test)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint('Mean squared error: %.2f'\n % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# The coefficient of determination: 1 is perfect prediction\nprint('Coefficient of determination: %.2f'\n % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test, color='black')\nplt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
`30`	`30`	`]`
`31`	`31`	`}`
`32`	`32`	`],`