DT/RF: Fix user doc examples
Project: http://git-wip-us.apache.org/repos/asf/madlib/repo Commit: http://git-wip-us.apache.org/repos/asf/madlib/commit/186390f7 Tree: http://git-wip-us.apache.org/repos/asf/madlib/tree/186390f7 Diff: http://git-wip-us.apache.org/repos/asf/madlib/diff/186390f7 Branch: refs/heads/master Commit: 186390f7c2af5ad886a4d5b77d0792b68cd3414d Parents: 1aac377 Author: Frank McQuillan <fmcquil...@pivotal.io> Authored: Wed Aug 1 12:49:10 2018 -0700 Committer: Rahul Iyer <ri...@apache.org> Committed: Wed Aug 1 12:58:44 2018 -0700 ---------------------------------------------------------------------- .../recursive_partitioning/decision_tree.sql_in | 16 ++++++++++------ .../recursive_partitioning/random_forest.sql_in | 12 +++++++----- 2 files changed, 17 insertions(+), 11 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/madlib/blob/186390f7/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in ---------------------------------------------------------------------- diff --git a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in index 469f1b2..5926152 100644 --- a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in +++ b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in @@ -284,14 +284,17 @@ tree_train( <th>impurity_var_importance</th> <td>DOUBLE PRECISION[]. Impurity importance of each variable. The order of the variables is the same as - that of 'independent_varnames' column in the summary table (see below). + that of the 'independent_varnames' column in the summary table (see below). The impurity importance of any feature is the decrease in impurity by a node containing the feature as a primary split, summed over the whole tree. If surrogates are used, then the importance value includes the impurity decrease scaled by the adjusted surrogate agreement. - Reported importance values are normalized to sum to 100 across - all variables. + Importance values are displayed as raw values as per the 'split_criterion' + parameter. + To see importance values normalized to sum to 100 across + all variables, use the importance display helper function + described later on this page. Please refer to [1] for more information on variable importance. </td> </tr> @@ -727,7 +730,7 @@ independent_var_types | text, boolean, double precision n_folds | 0 null_proxy | </pre> -View the impurity importance table using the helper function: +View the normalized impurity importance table using the helper function: <pre class="example"> \\x off DROP TABLE IF EXISTS imp_output; @@ -1111,10 +1114,11 @@ which shows ordering of levels of categorical variables 'vs' and 'cyl': SELECT pruning_cp, cat_levels_in_text, cat_n_levels, impurity_var_importance, tree_depth FROM train_output; </pre> <pre class="result"> +-[ RECORD 1 ]-----------+------------------------------------------------------------------------ pruning_cp | 0 cat_levels_in_text | {0,1,4,6,8} cat_n_levels | {2,3} -impurity_var_importance | {0,51.8593201959496,10.976977929129,5.31897402755374,31.8447278473677} +impurity_var_importance | {0,22.6309172500675,4.79024943310651,2.32115000000003,13.8967382920111} tree_depth | 4 </pre> View the summary table: @@ -1147,7 +1151,7 @@ independent_var_types | integer, integer, double precision, double precisi n_folds | 0 null_proxy | </pre> -View the impurity importance table using the helper function: +View the normalized impurity importance table using the helper function: <pre class="example"> \\x off DROP TABLE IF EXISTS imp_output; http://git-wip-us.apache.org/repos/asf/madlib/blob/186390f7/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in ---------------------------------------------------------------------- diff --git a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in index 39b6f5d..5b5a0f0 100644 --- a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in +++ b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in @@ -164,7 +164,9 @@ forest_train(training_table_name, Due to nature of permutation, the importance value can end up being negative if the number of levels for a categorical variable is small and is unbalanced. In such a scenario, the importance values are shifted to ensure - that the lowest importance value is 0. + that the lowest importance value is 0. To see importance values normalized + to sum to 100 across all variables, use the importance display helper function + described later on this page. </DD> @@ -758,7 +760,7 @@ the variables in 'independent_varnames' in <model_table>_summary. A higher value means higher importance for the variable. We can use the helper function to -get a better view of variable importance: +get a normalized view of variable importance: <pre class="example"> \\x off DROP TABLE IF EXISTS imp_output; @@ -1160,7 +1162,7 @@ oob_error | 16.5197718747446 oob_var_importance | {5.22711111111111,10.0872041666667,9.6875362244898,3.97782,2.99447839506173} impurity_var_importance | {5.1269704861111,7.04765974920884,20.9817274159476,4.02800949238769,10.5539079705215} </pre> -Use the helper function to display variable importance: +Use the helper function to display normalized variable importance: <pre class="example"> \\x off DROP TABLE IF EXISTS mt_imp_output; @@ -1347,14 +1349,14 @@ View the summary table: SELECT * FROM train_output_group; </pre> <pre class='result'> --[ RECORD 1 ]-----------+----------------------------------------------------- +-[ RECORD 1 ]-----------+----------------------------------------- gid | 1 success | t cat_n_levels | {2,2,2} cat_levels_in_text | {US,__NULL__,rainy,__NULL__,NY,__NULL__} oob_error | 1.00000000000000000000 oob_var_importance | {0,0,0} -impurity_var_importance | {32.1752184623349,25.2686155402256,22.5560374792348} +impurity_var_importance | {0.125,0.0944444444444,0.1836666666667} </pre> -# Predict for data not previously seen by assuming NULL
