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<div class="header">
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<a href="#func-members">Functions</a>  </div>
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<div class="title">c45.sql_in File Reference</div>  </div>
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<p>C4.5 APIs and main controller written in PL/PGSQL.  
<a href="#details">More...</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:a4fbee855d22101d15d195d573189eb98"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name, text validation_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float confidence_level, text how2handle_missing_value, int max_tree_depth, float node_prune_threshold, float node_split_threshold, int verbosity)</td></tr>
<tr class="memdesc:a4fbee855d22101d15d195d573189eb98"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the long form API of training tree with all specified parameters.  <a href="#a4fbee855d22101d15d195d573189eb98">More...</a><br /></td></tr>
<tr class="separator:a4fbee855d22101d15d195d573189eb98"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6c039416b94686b915e2a4c1133a5d44"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a6c039416b94686b915e2a4c1133a5d44">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name, text validation_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float confidence_level, text how2handle_missing_value)</td></tr>
<tr class="memdesc:a6c039416b94686b915e2a4c1133a5d44"><td class="mdescLeft">&#160;</td><td class="mdescRight">C45 train algorithm in short form.  <a href="#a6c039416b94686b915e2a4c1133a5d44">More...</a><br /></td></tr>
<tr class="separator:a6c039416b94686b915e2a4c1133a5d44"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a18b30ff1a063e7cd16274bf7ab2a71dc">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name)</td></tr>
<tr class="memdesc:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="mdescLeft">&#160;</td><td class="mdescRight">C45 train algorithm in short form.  <a href="#a18b30ff1a063e7cd16274bf7ab2a71dc">More...</a><br /></td></tr>
<tr class="separator:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac71787c47795b3b0b133cdbd37438242"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45_genrule</a> (text tree_table_name, int verbosity)</td></tr>
<tr class="memdesc:ac71787c47795b3b0b133cdbd37438242"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with rules.  <a href="#ac71787c47795b3b0b133cdbd37438242">More...</a><br /></td></tr>
<tr class="separator:ac71787c47795b3b0b133cdbd37438242"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acdba07d3897356a75666aa6d5999f490"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#acdba07d3897356a75666aa6d5999f490">c45_genrule</a> (text tree_table_name)</td></tr>
<tr class="memdesc:acdba07d3897356a75666aa6d5999f490"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with rules.  <a href="#acdba07d3897356a75666aa6d5999f490">More...</a><br /></td></tr>
<tr class="separator:acdba07d3897356a75666aa6d5999f490"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a32d2bcbc016c990991d77b6f6148306d"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45_display</a> (text tree_table, int max_depth)</td></tr>
<tr class="memdesc:a32d2bcbc016c990991d77b6f6148306d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with human readable format.  <a href="#a32d2bcbc016c990991d77b6f6148306d">More...</a><br /></td></tr>
<tr class="separator:a32d2bcbc016c990991d77b6f6148306d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad7f190eb8e5d53f4772fac699787c0fe"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ad7f190eb8e5d53f4772fac699787c0fe">c45_display</a> (text tree_table)</td></tr>
<tr class="memdesc:ad7f190eb8e5d53f4772fac699787c0fe"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the whole trained decision tree model with human readable format.  <a href="#ad7f190eb8e5d53f4772fac699787c0fe">More...</a><br /></td></tr>
<tr class="separator:ad7f190eb8e5d53f4772fac699787c0fe"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afe136e52f498f2ff9e2b91e38e29d670"><td class="memItemLeft" align="right" valign="top">c45_classify_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45_classify</a> (text tree_table_name, text classification_table_name, text result_table_name, int verbosity)</td></tr>
<tr class="memdesc:afe136e52f498f2ff9e2b91e38e29d670"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify dataset using trained decision tree model. The classification result will be stored in the table which is defined as:  <a href="#afe136e52f498f2ff9e2b91e38e29d670">More...</a><br /></td></tr>
<tr class="separator:afe136e52f498f2ff9e2b91e38e29d670"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af5eb174eeecd11233409657221586cf1"><td class="memItemLeft" align="right" valign="top">c45_classify_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#af5eb174eeecd11233409657221586cf1">c45_classify</a> (text tree_table_name, text classification_table_name, text result_table_name)</td></tr>
<tr class="memdesc:af5eb174eeecd11233409657221586cf1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify dataset using trained decision tree model. It runs in quiet mode. The classification result will be stored in the table which is defined as:  <a href="#af5eb174eeecd11233409657221586cf1">More...</a><br /></td></tr>
<tr class="separator:af5eb174eeecd11233409657221586cf1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1b634db47e9006d114da0987e80b9601"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45_score</a> (text tree_table_name, text scoring_table_name, int verbosity)</td></tr>
<tr class="memdesc:a1b634db47e9006d114da0987e80b9601"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check the accuracy of the decision tree model.  <a href="#a1b634db47e9006d114da0987e80b9601">More...</a><br /></td></tr>
<tr class="separator:a1b634db47e9006d114da0987e80b9601"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af0739749507c1097003dcf529d29fee2"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#af0739749507c1097003dcf529d29fee2">c45_score</a> (text tree_table_name, text scoring_table_name)</td></tr>
<tr class="memdesc:af0739749507c1097003dcf529d29fee2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check the accuracy of the decision tree model.  <a href="#af0739749507c1097003dcf529d29fee2">More...</a><br /></td></tr>
<tr class="separator:af0739749507c1097003dcf529d29fee2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac25e17ecbc70149aa559018e718fc793"><td class="memItemLeft" align="right" valign="top">boolean&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45_clean</a> (text result_tree_table_name)</td></tr>
<tr class="memdesc:ac25e17ecbc70149aa559018e718fc793"><td class="mdescLeft">&#160;</td><td class="mdescRight">Cleanup the trained tree table and any relevant tables.  <a href="#ac25e17ecbc70149aa559018e718fc793">More...</a><br /></td></tr>
<tr class="separator:ac25e17ecbc70149aa559018e718fc793"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><dl class="section date"><dt>Date</dt><dd>April 5, 2012</dd></dl>
<dl class="section see"><dt>See also</dt><dd>For a brief introduction to decision trees, see the module description <a class="el" href="group__grp__dectree.html">Decision Tree (old C4.5 implementation)</a>. </dd></dl>
</div><h2 class="groupheader">Function Documentation</h2>
<a class="anchor" id="afe136e52f498f2ff9e2b91e38e29d670"></a>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">c45_classify_result c45_classify </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>classification_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>result_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>verbosity</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>CREATE TABLE classification_result ( id INT|BIGINT, class SUPPORTED_DATA_TYPE, prob FLOAT );</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of trained tree. </td></tr>
    <tr><td class="paramname">classification_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">result_table_name</td><td>The name of result table. </td></tr>
    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A c45_classify_result object. </dd></dl>

</div>
</div>
<a class="anchor" id="af5eb174eeecd11233409657221586cf1"></a>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">c45_classify_result c45_classify </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>classification_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>result_table_name</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>CREATE TABLE classification_result ( id INT|BIGINT, class SUPPORTED_DATA_TYPE, prob FLOAT );</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of trained tree. </td></tr>
    <tr><td class="paramname">classification_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">result_table_name</td><td>The name of result table.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A c45_classify_result object. </dd></dl>

</div>
</div>
<a class="anchor" id="ac25e17ecbc70149aa559018e718fc793"></a>
<div class="memitem">
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      <table class="memname">
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          <td class="memname">boolean c45_clean </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>result_tree_table_name</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table containing the tree's information.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The status of that cleanup operation. </dd></dl>

</div>
</div>
<a class="anchor" id="a32d2bcbc016c990991d77b6f6148306d"></a>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">set&lt;text&gt; c45_display </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_depth</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table</td><td>The name of the table containing the tree's information. </td></tr>
    <tr><td class="paramname">max_depth</td><td>The max depth to be displayed. If null, this function will show all levels.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The text representing the tree with human readable format. </dd></dl>

</div>
</div>
<a class="anchor" id="ad7f190eb8e5d53f4772fac699787c0fe"></a>
<div class="memitem">
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      <table class="memname">
        <tr>
          <td class="memname">set&lt;text&gt; c45_display </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table</td><td>The name of the table containing the tree's information.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The text representing the tree with human readable format. </dd></dl>

</div>
</div>
<a class="anchor" id="ac71787c47795b3b0b133cdbd37438242"></a>
<div class="memitem">
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          <td class="memname">set&lt;text&gt; c45_genrule </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table_name</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>verbosity</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of the table containing the tree's information. </td></tr>
    <tr><td class="paramname">verbosity</td><td>If &gt;= 1 will run in verbose mode.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The rule representation text for a decision tree. </dd></dl>

</div>
</div>
<a class="anchor" id="acdba07d3897356a75666aa6d5999f490"></a>
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          <td class="memname">set&lt;text&gt; c45_genrule </td>
          <td>(</td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>tree_table_name</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of the table containing the tree's information.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The rule representation text for a decision tree. </dd></dl>

</div>
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          <td class="memname">float8 c45_score </td>
          <td>(</td>
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          <td class="paramname"><em>tree_table_name</em>, </td>
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        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>scoring_table_name</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>verbosity</em>&#160;</td>
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          <td></td>
          <td>)</td>
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</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of the trained tree. </td></tr>
    <tr><td class="paramname">scoring_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The estimated accuracy information. </dd></dl>

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<a class="anchor" id="af0739749507c1097003dcf529d29fee2"></a>
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          <td class="memname">float8 c45_score </td>
          <td>(</td>
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          <td class="paramname"><em>tree_table_name</em>, </td>
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        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>scoring_table_name</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">tree_table_name</td><td>The name of the trained tree. </td></tr>
    <tr><td class="paramname">scoring_table_name</td><td>The name of the table/view with the source data.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The estimated accuracy information. </dd></dl>

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          <td class="memname">c45_train_result c45_train </td>
          <td>(</td>
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          <td class="paramname"><em>split_criterion</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>training_table_name</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>result_tree_table_name</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>validation_table_name</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>continuous_feature_names</em>, </td>
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          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>feature_col_names</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>id_col_name</em>, </td>
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          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>class_col_name</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>confidence_level</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>how2handle_missing_value</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_tree_depth</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>node_prune_threshold</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>node_split_threshold</em>, </td>
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          <td class="paramtype">int&#160;</td>
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<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. The valid values are ‘infogain’, ‘gainratio’, and ‘gini’. It can't be NULL. Information gain(infogain) and gini index(gini) are biased toward multivalued attributes. Gain ratio(gainratio) adjusts for this bias. However, it tends to prefer unbalanced splits in which one partition is much smaller than the others. </td></tr>
    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept. </td></tr>
    <tr><td class="paramname">validation_table_name</td><td>The name of the table/view that contains the validation set used for tree pruning. The default is NULL, in which case we will not do tree pruning. </td></tr>
    <tr><td class="paramname">continuous_feature_names</td><td>A comma-separated list of the names of features whose values are continuous. The default is null, which means there are no continuous features in the training table. </td></tr>
    <tr><td class="paramname">feature_col_names</td><td>A comma-separated list of the names of table columns, each of which defines a feature. The default value is null, which means all the columns in the training table, except columns named ‘id’ and ‘class’, will be used as features. </td></tr>
    <tr><td class="paramname">id_col_name</td><td>The name of the column containing an ID for each record. </td></tr>
    <tr><td class="paramname">class_col_name</td><td>The name of the column containing the labeled class. </td></tr>
    <tr><td class="paramname">confidence_level</td><td>A statistical confidence interval of the resubstitution error. </td></tr>
    <tr><td class="paramname">how2handle_missing_value</td><td>The way to handle missing value. The valid value is 'explicit' or 'ignore'. </td></tr>
    <tr><td class="paramname">max_tree_depth</td><td>Specifies the maximum number of levels in the result DT to avoid overgrown DTs. </td></tr>
    <tr><td class="paramname">node_prune_threshold</td><td>The minimum percentage of the number of records required in a child node. It can't be NULL. The range of it is in [0.0, 1.0]. This threshold only applies to the non-root nodes. Therefore, if its value is 1, then the trained tree only has one node (the root node); if its value is 0, then no nodes will be pruned by this parameter. </td></tr>
    <tr><td class="paramname">node_split_threshold</td><td>The minimum percentage of the number of records required in a node in order for a further split to be possible. It can't be NULL. The range of it is in [0.0, 1.0]. If it's value is 1, then the trained tree only has two levels, since only the root node can grow; if its value is 0, then trees can grow extensively. </td></tr>
    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
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  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object. </dd></dl>

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          <td class="paramtype">text&#160;</td>
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          <td class="paramname"><em>continuous_feature_names</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>feature_col_names</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramname"><em>id_col_name</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramname"><em>class_col_name</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>how2handle_missing_value</em>&#160;</td>
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        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
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</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. Possible values are ‘gain’, ‘gainratio’, and ‘gini’. </td></tr>
    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept. </td></tr>
    <tr><td class="paramname">validation_table_name</td><td>The name of the table/view that contains the validation set used for tree pruning. The default is NULL, in which case we will not do tree pruning. </td></tr>
    <tr><td class="paramname">continuous_feature_names</td><td>A comma-separated list of the names of features whose values are continuous. The default is null, which means there are no continuous features in the training table. </td></tr>
    <tr><td class="paramname">feature_col_names</td><td>A comma-separated list of the names of table columns, each of which defines a feature. The default value is null, which means all the columns in the training table, except columns named ‘id’ and ‘class’, will be used as features. </td></tr>
    <tr><td class="paramname">id_col_name</td><td>The name of the column containing an ID for each record. </td></tr>
    <tr><td class="paramname">class_col_name</td><td>The name of the column containing the labeled class. </td></tr>
    <tr><td class="paramname">confidence_level</td><td>A statistical confidence interval of the resubstitution error. </td></tr>
    <tr><td class="paramname">how2handle_missing_value</td><td>The way to handle missing value. The valid value is 'explicit' or 'ignore'.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object.</dd></dl>
<dl class="section note"><dt>Note</dt><dd>This calls the long form of C45 with the following default parameters:<ul>
<li>max_tree_deapth := 10</li>
<li>node_prune_threshold := 0.001</li>
<li>node_split_threshold := 0.01</li>
<li>verbosity := 0 </li>
</ul>
</dd></dl>

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</div>
<a class="anchor" id="a18b30ff1a063e7cd16274bf7ab2a71dc"></a>
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      <table class="memname">
        <tr>
          <td class="memname">c45_train_result c45_train </td>
          <td>(</td>
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          <td class="paramname"><em>split_criterion</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>training_table_name</em>, </td>
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        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">text&#160;</td>
          <td class="paramname"><em>result_tree_table_name</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. Possible values are ‘gain’, ‘gainratio’, and ‘gini’. </td></tr>
    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object.</dd></dl>
<dl class="section note"><dt>Note</dt><dd>This calls the above short form of C45 with the following default parameters:<ul>
<li>validation_table_name := NULL</li>
<li>continuous_feature_names := NULL</li>
<li>id_column_name := 'id'</li>
<li>class_column_name := 'class'</li>
<li>confidence_level := 25</li>
<li>how2handle_missing_value := 'explicit'</li>
<li>max_tree_deapth := 10</li>
<li>node_prune_threshold := 0.001</li>
<li>node_split_threshold := 0.01</li>
<li>verbosity := 0 </li>
</ul>
</dd></dl>

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