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Posted to issues@madlib.apache.org by "Frank McQuillan (JIRA)" <ji...@apache.org> on 2019/06/06 00:43:00 UTC

[jira] [Updated] (MADLIB-1318) DT error when tree depth increases past a certain limit

     [ https://issues.apache.org/jira/browse/MADLIB-1318?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan updated MADLIB-1318:
------------------------------------
    Fix Version/s:     (was: v1.16)
                   v2.0

> DT error when tree depth increases past a certain limit
> -------------------------------------------------------
>
>                 Key: MADLIB-1318
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1318
>             Project: Apache MADlib
>          Issue Type: Bug
>          Components: Module: Decision Tree
>            Reporter: Frank McQuillan
>            Priority: Major
>             Fix For: v2.0
>
>
> drop table if exists out1;
> drop table if exists out1_summary;
> select madlib.tree_train(
> 'training_data',  -- training data table
> 'out1', -- model output table
> 'id', -- id column
> 'response',  -- dependent var
> '...', -- list of features
> NULL::text, -- features to exclude
> 'gini', -- split criteria
> NULL::text, -- grouping
> NULL::text, -- weights
> 20, -- max depth of tree
> 3, -- min split 
> 1,  -- num bucket
> 100,  -- num splits
> 'cp=0.000001', -- pruning parameters
> 'max_surrogates=5' -- by default, this is 0, all rows containing any NULL predictor values will not be used in modeling or prediction
> );
> {code}
> ERROR:  plpy.Error: unrecognized error in PLy_spi_execute_fetch_result (plpython.c:5038)
> CONTEXT:  Traceback (most recent call last):
>   PL/Python function "tree_train", line 25, in <module>
>     null_handling_params, verbose_mode)
>   PL/Python function "tree_train", line 529, in tree_train
>   PL/Python function "tree_train", line 296, in _get_tree_states
>   PL/Python function "tree_train", line 2293, in _tree_train_using_bins
>   PL/Python function "tree_train", line 1226, in _one_step
> PL/Python function "tree_train"
> {code}



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