Pushing the docs to dev/ for branch: master, commit c84ad60c8cfb007d8ce158d24177355bda8dffa1

Author: sklearn-ci

dev/_downloads/1fdea43ae2c6f41584a8f0032d7ed247/plot_gradient_boosting_quantile.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import GradientBoostingRegressor\n\nnp.random.seed(1)\n\n\ndef f(x):\n    \"\"\"The function to predict.\"\"\"\n    return x * np.sin(x)\n\n#----------------------------------------------------------------------\n#  First the noiseless case\nX = np.atleast_2d(np.random.uniform(0, 10.0, size=100)).T\nX = X.astype(np.float32)\n\n# Observations\ny = f(X).ravel()\n\ndy = 1.5 + 1.0 * np.random.random(y.shape)\nnoise = np.random.normal(0, dy)\ny += noise\ny = y.astype(np.float32)\n\n# Mesh the input space for evaluations of the real function, the prediction and\n# its MSE\nxx = np.atleast_2d(np.linspace(0, 10, 1000)).T\nxx = xx.astype(np.float32)\n\nalpha = 0.95\n\nclf = GradientBoostingRegressor(loss='quantile', alpha=alpha,\n                                n_estimators=250, max_depth=3,\n                                learning_rate=.1, min_samples_leaf=9,\n                                min_samples_split=9)\n\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_upper = clf.predict(xx)\n\nclf.set_params(alpha=1.0 - alpha)\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_lower = clf.predict(xx)\n\nclf.set_params(loss='ls')\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_pred = clf.predict(xx)\n\n# Plot the function, the prediction and the 90% confidence interval based on\n# the MSE\nfig = plt.figure()\nplt.plot(xx, f(xx), 'g:', label=r'$f(x) = x\\,\\sin(x)$')\nplt.plot(X, y, 'b.', markersize=10, label=u'Observations')\nplt.plot(xx, y_pred, 'r-', label=u'Prediction')\nplt.plot(xx, y_upper, 'k-')\nplt.plot(xx, y_lower, 'k-')\nplt.fill(np.concatenate([xx, xx[::-1]]),\n         np.concatenate([y_upper, y_lower[::-1]]),\n         alpha=.5, fc='b', ec='None', label='90% prediction interval')\nplt.xlabel('$x$')\nplt.ylabel('$f(x)$')\nplt.ylim(-10, 20)\nplt.legend(loc='upper left')\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import GradientBoostingRegressor\n\nnp.random.seed(1)\n\n\ndef f(x):\n    \"\"\"The function to predict.\"\"\"\n    return x * np.sin(x)\n\n#----------------------------------------------------------------------\n#  First the noiseless case\nX = np.atleast_2d(np.random.uniform(0, 10.0, size=100)).T\nX = X.astype(np.float32)\n\n# Observations\ny = f(X).ravel()\n\ndy = 1.5 + 1.0 * np.random.random(y.shape)\nnoise = np.random.normal(0, dy)\ny += noise\ny = y.astype(np.float32)\n\n# Mesh the input space for evaluations of the real function, the prediction and\n# its MSE\nxx = np.atleast_2d(np.linspace(0, 10, 1000)).T\nxx = xx.astype(np.float32)\n\nalpha = 0.95\n\nclf = GradientBoostingRegressor(loss='quantile', alpha=alpha,\n                                n_estimators=250, max_depth=3,\n                                learning_rate=.1, min_samples_leaf=9,\n                                min_samples_split=9)\n\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_upper = clf.predict(xx)\n\nclf.set_params(alpha=1.0 - alpha)\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_lower = clf.predict(xx)\n\nclf.set_params(loss='ls')\nclf.fit(X, y)\n\n# Make the prediction on the meshed x-axis\ny_pred = clf.predict(xx)\n\n# Plot the function, the prediction and the 95% confidence interval based on\n# the MSE\nfig = plt.figure()\nplt.plot(xx, f(xx), 'g:', label=r'$f(x) = x\\,\\sin(x)$')\nplt.plot(X, y, 'b.', markersize=10, label=u'Observations')\nplt.plot(xx, y_pred, 'r-', label=u'Prediction')\nplt.plot(xx, y_upper, 'k-')\nplt.plot(xx, y_lower, 'k-')\nplt.fill(np.concatenate([xx, xx[::-1]]),\n         np.concatenate([y_upper, y_lower[::-1]]),\n         alpha=.5, fc='b', ec='None', label='95% prediction interval')\nplt.xlabel('$x$')\nplt.ylabel('$f(x)$')\nplt.ylim(-10, 20)\nplt.legend(loc='upper left')\nplt.show()"
       ]
     }
   ],

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -61,7 +61,7 @@ def f(x):
 # Make the prediction on the meshed x-axis
 y_pred = clf.predict(xx)
 
-# Plot the function, the prediction and the 90% confidence interval based on
+# Plot the function, the prediction and the 95% confidence interval based on
 # the MSE
 fig = plt.figure()
 plt.plot(xx, f(xx), 'g:', label=r'$f(x) = x\,\sin(x)$')
@@ -71,7 +71,7 @@ def f(x):
 plt.plot(xx, y_lower, 'k-')
 plt.fill(np.concatenate([xx, xx[::-1]]),
          np.concatenate([y_upper, y_lower[::-1]]),
-         alpha=.5, fc='b', ec='None', label='90% prediction interval')
+         alpha=.5, fc='b', ec='None', label='95% prediction interval')
 plt.xlabel('$x$')
 plt.ylabel('$f(x)$')
 plt.ylim(-10, 20)