Pushing the docs to dev/ for branch: main, commit f2231993675134ca7d01b1b8ebfd6d082372272e

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -16,10 +16,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn import linear_model
+from sklearn.linear_model import LogisticRegression, LinearRegression
 from scipy.special import expit
 
-# General a toy dataset:s it's just a straight line with some Gaussian noise:
+# Generate a toy dataset, it's just a straight line with some Gaussian noise:
 xmin, xmax = -5, 5
 n_samples = 100
 np.random.seed(0)
@@ -31,7 +31,7 @@
 X = X[:, np.newaxis]
 
 # Fit the classifier
-clf = linear_model.LogisticRegression(C=1e5)
+clf = LogisticRegression(C=1e5)
 clf.fit(X, y)
 
 # and plot the result
@@ -43,7 +43,7 @@
 loss = expit(X_test * clf.coef_ + clf.intercept_).ravel()
 plt.plot(X_test, loss, color="red", linewidth=3)
 
-ols = linear_model.LinearRegression()
+ols = LinearRegression()
 ols.fit(X, y)
 plt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)
 plt.axhline(0.5, color=".5")

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\nfrom scipy.special import expit\n\n# General a toy dataset:s it's just a straight line with some Gaussian noise:\nxmin, xmax = -5, 5\nn_samples = 100\nnp.random.seed(0)\nX = np.random.normal(size=n_samples)\ny = (X > 0).astype(float)\nX[X > 0] *= 4\nX += 0.3 * np.random.normal(size=n_samples)\n\nX = X[:, np.newaxis]\n\n# Fit the classifier\nclf = linear_model.LogisticRegression(C=1e5)\nclf.fit(X, y)\n\n# and plot the result\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.scatter(X.ravel(), y, color=\"black\", zorder=20)\nX_test = np.linspace(-5, 10, 300)\n\nloss = expit(X_test * clf.coef_ + clf.intercept_).ravel()\nplt.plot(X_test, loss, color=\"red\", linewidth=3)\n\nols = linear_model.LinearRegression()\nols.fit(X, y)\nplt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\nplt.axhline(0.5, color=\".5\")\n\nplt.ylabel(\"y\")\nplt.xlabel(\"X\")\nplt.xticks(range(-5, 10))\nplt.yticks([0, 0.5, 1])\nplt.ylim(-0.25, 1.25)\nplt.xlim(-4, 10)\nplt.legend(\n    (\"Logistic Regression Model\", \"Linear Regression Model\"),\n    loc=\"lower right\",\n    fontsize=\"small\",\n)\nplt.tight_layout()\nplt.show()"
+        "# Code source: Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import LogisticRegression, LinearRegression\nfrom scipy.special import expit\n\n# Generate a toy dataset, it's just a straight line with some Gaussian noise:\nxmin, xmax = -5, 5\nn_samples = 100\nnp.random.seed(0)\nX = np.random.normal(size=n_samples)\ny = (X > 0).astype(float)\nX[X > 0] *= 4\nX += 0.3 * np.random.normal(size=n_samples)\n\nX = X[:, np.newaxis]\n\n# Fit the classifier\nclf = LogisticRegression(C=1e5)\nclf.fit(X, y)\n\n# and plot the result\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.scatter(X.ravel(), y, color=\"black\", zorder=20)\nX_test = np.linspace(-5, 10, 300)\n\nloss = expit(X_test * clf.coef_ + clf.intercept_).ravel()\nplt.plot(X_test, loss, color=\"red\", linewidth=3)\n\nols = LinearRegression()\nols.fit(X, y)\nplt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\nplt.axhline(0.5, color=\".5\")\n\nplt.ylabel(\"y\")\nplt.xlabel(\"X\")\nplt.xticks(range(-5, 10))\nplt.yticks([0, 0.5, 1])\nplt.ylim(-0.25, 1.25)\nplt.xlim(-4, 10)\nplt.legend(\n    (\"Logistic Regression Model\", \"Linear Regression Model\"),\n    loc=\"lower right\",\n    fontsize=\"small\",\n)\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],