Pushing the docs to dev/ for branch: master, commit b70472bd241cb28ac8acfc4e69ccab61acccf14b

Author: sklearn-ci

dev/_downloads/33bc25666e895f6720c86dffe127d651/plot_randomized_search.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Comparing randomized search and grid search for hyperparameter estimation\n\n\nCompare randomized search and grid search for optimizing hyperparameters of a\nrandom forest.\nAll parameters that influence the learning are searched simultaneously\n(except for the number of estimators, which poses a time / quality tradeoff).\n\nThe randomized search and the grid search explore exactly the same space of\nparameters. The result in parameter settings is quite similar, while the run\ntime for randomized search is drastically lower.\n\nThe performance is may slightly worse for the randomized search, and is likely\ndue to a noise effect and would not carry over to a held-out test set.\n\nNote that in practice, one would not search over this many different parameters\nsimultaneously using grid search, but pick only the ones deemed most important.\n"
+        "\n# Comparing randomized search and grid search for hyperparameter estimation\n\n\nCompare randomized search and grid search for optimizing hyperparameters of a\nlinear SVM with SGD training.\nAll parameters that influence the learning are searched simultaneously\n(except for the number of estimators, which poses a time / quality tradeoff).\n\nThe randomized search and the grid search explore exactly the same space of\nparameters. The result in parameter settings is quite similar, while the run\ntime for randomized search is drastically lower.\n\nThe performance is may slightly worse for the randomized search, and is likely\ndue to a noise effect and would not carry over to a held-out test set.\n\nNote that in practice, one would not search over this many different parameters\nsimultaneously using grid search, but pick only the ones deemed most important.\n"
       ]
     },
     {

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -4,7 +4,7 @@
 =========================================================================
 
 Compare randomized search and grid search for optimizing hyperparameters of a
-random forest.
+linear SVM with SGD training.
 All parameters that influence the learning are searched simultaneously
 (except for the number of estimators, which poses a time / quality tradeoff).