Pushing the docs to dev/ for branch: master, commit 59612a22b3e7d87f81938707a835b860d101ff9f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Tim Head <[email protected]>\n#\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.multioutput import MultiOutputRegressor\n\n\n# Create a random dataset\nrng = np.random.RandomState(1)\nX = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\ny = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\ny += (0.5 - rng.rand(*y.shape))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=400, test_size=200, random_state=4)\n\nmax_depth = 30\nregr_multirf = MultiOutputRegressor(RandomForestRegressor(max_depth=max_depth,\n                                                          random_state=0))\nregr_multirf.fit(X_train, y_train)\n\nregr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2)\nregr_rf.fit(X_train, y_train)\n\n# Predict on new data\ny_multirf = regr_multirf.predict(X_test)\ny_rf = regr_rf.predict(X_test)\n\n# Plot the results\nplt.figure()\ns = 50\na = 0.4\nplt.scatter(y_test[:, 0], y_test[:, 1], edgecolor='k',\n            c=\"navy\", s=s, marker=\"s\", alpha=a, label=\"Data\")\nplt.scatter(y_multirf[:, 0], y_multirf[:, 1], edgecolor='k',\n            c=\"cornflowerblue\", s=s, alpha=a,\n            label=\"Multi RF score=%.2f\" % regr_multirf.score(X_test, y_test))\nplt.scatter(y_rf[:, 0], y_rf[:, 1], edgecolor='k',\n            c=\"c\", s=s, marker=\"^\", alpha=a,\n            label=\"RF score=%.2f\" % regr_rf.score(X_test, y_test))\nplt.xlim([-6, 6])\nplt.ylim([-6, 6])\nplt.xlabel(\"target 1\")\nplt.ylabel(\"target 2\")\nplt.title(\"Comparing random forests and the multi-output meta estimator\")\nplt.legend()\nplt.show()"
+        "print(__doc__)\n\n# Author: Tim Head <[email protected]>\n#\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.multioutput import MultiOutputRegressor\n\n\n# Create a random dataset\nrng = np.random.RandomState(1)\nX = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\ny = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\ny += (0.5 - rng.rand(*y.shape))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=400, test_size=200, random_state=4)\n\nmax_depth = 30\nregr_multirf = MultiOutputRegressor(RandomForestRegressor(n_estimators=100,\n                                                          max_depth=max_depth,\n                                                          random_state=0))\nregr_multirf.fit(X_train, y_train)\n\nregr_rf = RandomForestRegressor(n_estimators=100, max_depth=max_depth,\n                                random_state=2)\nregr_rf.fit(X_train, y_train)\n\n# Predict on new data\ny_multirf = regr_multirf.predict(X_test)\ny_rf = regr_rf.predict(X_test)\n\n# Plot the results\nplt.figure()\ns = 50\na = 0.4\nplt.scatter(y_test[:, 0], y_test[:, 1], edgecolor='k',\n            c=\"navy\", s=s, marker=\"s\", alpha=a, label=\"Data\")\nplt.scatter(y_multirf[:, 0], y_multirf[:, 1], edgecolor='k',\n            c=\"cornflowerblue\", s=s, alpha=a,\n            label=\"Multi RF score=%.2f\" % regr_multirf.score(X_test, y_test))\nplt.scatter(y_rf[:, 0], y_rf[:, 1], edgecolor='k',\n            c=\"c\", s=s, marker=\"^\", alpha=a,\n            label=\"RF score=%.2f\" % regr_rf.score(X_test, y_test))\nplt.xlim([-6, 6])\nplt.ylim([-6, 6])\nplt.xlabel(\"target 1\")\nplt.ylabel(\"target 2\")\nplt.title(\"Comparing random forests and the multi-output meta estimator\")\nplt.legend()\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -43,11 +43,13 @@
     X, y, train_size=400, test_size=200, random_state=4)
 
 max_depth = 30
-regr_multirf = MultiOutputRegressor(RandomForestRegressor(max_depth=max_depth,
+regr_multirf = MultiOutputRegressor(RandomForestRegressor(n_estimators=100,
+                                                          max_depth=max_depth,
                                                           random_state=0))
 regr_multirf.fit(X_train, y_train)
 
-regr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2)
+regr_rf = RandomForestRegressor(n_estimators=100, max_depth=max_depth,
+                                random_state=2)
 regr_rf.fit(X_train, y_train)
 
 # Predict on new data