Pushing the docs to dev/ for branch: main, commit 9cfacf1540a991461b91617c779c69753a1ee4c0

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -43,15 +43,15 @@
 colors = ['r-', 'b-', 'y-', 'm-']
 
 x = np.linspace(X.min(), X.max(), 7)
-epsilon_values = [1.35, 1.5, 1.75, 1.9]
+epsilon_values = [1, 1.5, 1.75, 1.9]
 for k, epsilon in enumerate(epsilon_values):
     huber = HuberRegressor(alpha=0.0, epsilon=epsilon)
     huber.fit(X, y)
     coef_ = huber.coef_ * x + huber.intercept_
     plt.plot(x, coef_, colors[k], label="huber loss, %s" % epsilon)
 
 # Fit a ridge regressor to compare it to huber regressor.
-ridge = Ridge(alpha=0.0, random_state=0, normalize=True)
+ridge = Ridge(alpha=0.0, random_state=0)
 ridge.fit(X, y)
 coef_ridge = ridge.coef_
 coef_ = ridge.coef_ * x + ridge.intercept_

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Manoj Kumar [email protected]\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import HuberRegressor, Ridge\n\n# Generate toy data.\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples=20, n_features=1, random_state=0, noise=4.0,\n                       bias=100.0)\n\n# Add four strong outliers to the dataset.\nX_outliers = rng.normal(0, 0.5, size=(4, 1))\ny_outliers = rng.normal(0, 2.0, size=4)\nX_outliers[:2, :] += X.max() + X.mean() / 4.\nX_outliers[2:, :] += X.min() - X.mean() / 4.\ny_outliers[:2] += y.min() - y.mean() / 4.\ny_outliers[2:] += y.max() + y.mean() / 4.\nX = np.vstack((X, X_outliers))\ny = np.concatenate((y, y_outliers))\nplt.plot(X, y, 'b.')\n\n# Fit the huber regressor over a series of epsilon values.\ncolors = ['r-', 'b-', 'y-', 'm-']\n\nx = np.linspace(X.min(), X.max(), 7)\nepsilon_values = [1.35, 1.5, 1.75, 1.9]\nfor k, epsilon in enumerate(epsilon_values):\n    huber = HuberRegressor(alpha=0.0, epsilon=epsilon)\n    huber.fit(X, y)\n    coef_ = huber.coef_ * x + huber.intercept_\n    plt.plot(x, coef_, colors[k], label=\"huber loss, %s\" % epsilon)\n\n# Fit a ridge regressor to compare it to huber regressor.\nridge = Ridge(alpha=0.0, random_state=0, normalize=True)\nridge.fit(X, y)\ncoef_ridge = ridge.coef_\ncoef_ = ridge.coef_ * x + ridge.intercept_\nplt.plot(x, coef_, 'g-', label=\"ridge regression\")\n\nplt.title(\"Comparison of HuberRegressor vs Ridge\")\nplt.xlabel(\"X\")\nplt.ylabel(\"y\")\nplt.legend(loc=0)\nplt.show()"
+        "# Authors: Manoj Kumar [email protected]\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import HuberRegressor, Ridge\n\n# Generate toy data.\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples=20, n_features=1, random_state=0, noise=4.0,\n                       bias=100.0)\n\n# Add four strong outliers to the dataset.\nX_outliers = rng.normal(0, 0.5, size=(4, 1))\ny_outliers = rng.normal(0, 2.0, size=4)\nX_outliers[:2, :] += X.max() + X.mean() / 4.\nX_outliers[2:, :] += X.min() - X.mean() / 4.\ny_outliers[:2] += y.min() - y.mean() / 4.\ny_outliers[2:] += y.max() + y.mean() / 4.\nX = np.vstack((X, X_outliers))\ny = np.concatenate((y, y_outliers))\nplt.plot(X, y, 'b.')\n\n# Fit the huber regressor over a series of epsilon values.\ncolors = ['r-', 'b-', 'y-', 'm-']\n\nx = np.linspace(X.min(), X.max(), 7)\nepsilon_values = [1, 1.5, 1.75, 1.9]\nfor k, epsilon in enumerate(epsilon_values):\n    huber = HuberRegressor(alpha=0.0, epsilon=epsilon)\n    huber.fit(X, y)\n    coef_ = huber.coef_ * x + huber.intercept_\n    plt.plot(x, coef_, colors[k], label=\"huber loss, %s\" % epsilon)\n\n# Fit a ridge regressor to compare it to huber regressor.\nridge = Ridge(alpha=0.0, random_state=0)\nridge.fit(X, y)\ncoef_ridge = ridge.coef_\ncoef_ = ridge.coef_ * x + ridge.intercept_\nplt.plot(x, coef_, 'g-', label=\"ridge regression\")\n\nplt.title(\"Comparison of HuberRegressor vs Ridge\")\nplt.xlabel(\"X\")\nplt.ylabel(\"y\")\nplt.legend(loc=0)\nplt.show()"
       ]
     }
   ],