http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/eb070238/userguide/binaryclass/titanic_rf.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/titanic_rf.html b/userguide/binaryclass/titanic_rf.html index b76030d..74d2d5a 100644 --- a/userguide/binaryclass/titanic_rf.html +++ b/userguide/binaryclass/titanic_rf.html @@ -980,6 +980,21 @@ </li> + <li class="chapter " data-level="6.3.5" data-path="news20_rf.html"> + + <a href="news20_rf.html"> + + + <b>6.3.5.</b> + + Random Forest + + </a> + + + + </li> + </ul> @@ -1324,7 +1339,7 @@ <b>7.2.3.</b> - RandomForest + Random Forest </a> @@ -2122,58 +2137,59 @@ The example gives a baseline score without any feature engineering.</p> <li><a href="#training">Training</a></li> <li><a href="#prediction">Prediction</a></li> <li><a href="#kaggle-submission">Kaggle submission</a></li> +<li><a href="#graphvis-export">Graphvis export</a></li> <li><a href="#test-by-dividing-training-dataset">Test by dividing training dataset</a></li> </ul> </div><!-- tocstop --> <h1 id="data-preparation">Data preparation</h1> -<pre><code class="lang-sql">create database titanic; -use titanic; - -drop table train; -create external table train ( - passengerid int, -- unique id - survived int, -- target label - pclass int, - name string, - sex string, - age int, - sibsp int, -- Number of Siblings/Spouses Aboard - parch int, -- Number of Parents/Children Aboard - ticket string, - fare double, - cabin string, - embarked string +<pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">database</span> titanic; +<span class="hljs-keyword">use</span> titanic; + +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> train; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> train ( + passengerid <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- unique id</span> + survived <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- target label</span> + pclass <span class="hljs-built_in">int</span>, + <span class="hljs-keyword">name</span> <span class="hljs-keyword">string</span>, + sex <span class="hljs-keyword">string</span>, + age <span class="hljs-built_in">int</span>, + sibsp <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Siblings/Spouses Aboard</span> + parch <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Parents/Children Aboard</span> + ticket <span class="hljs-keyword">string</span>, + fare <span class="hljs-keyword">double</span>, + cabin <span class="hljs-keyword">string</span>, + embarked <span class="hljs-keyword">string</span> ) -ROW FORMAT DELIMITED - FIELDS TERMINATED BY '|' - LINES TERMINATED BY '\n' -STORED AS TEXTFILE LOCATION '/dataset/titanic/train'; - -hadoop fs -rm /dataset/titanic/train/train.csv -awk '{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$4=substr($4,2,length($4)-2);print $0}' train.csv | hadoop fs -put - /dataset/titanic/train/train.csv - -drop table test_raw; -create external table test_raw ( - passengerid int, - pclass int, - name string, - sex string, - age int, - sibsp int, -- Number of Siblings/Spouses Aboard - parch int, -- Number of Parents/Children Aboard - ticket string, - fare double, - cabin string, - embarked string +<span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> + <span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'|'</span> + <span class="hljs-keyword">LINES</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'\n'</span> +<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/dataset/titanic/train'</span>; +</code></pre> +<pre><code class="lang-sh">hadoop fs -rm /dataset/titanic/train/train.csv +awk <span class="hljs-string">'{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$4=substr($4,2,length($4)-2);print $0}'</span> train.csv | hadoop fs -put - /dataset/titanic/train/train.csv +</code></pre> +<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> test_raw; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> test_raw ( + passengerid <span class="hljs-built_in">int</span>, + pclass <span class="hljs-built_in">int</span>, + <span class="hljs-keyword">name</span> <span class="hljs-keyword">string</span>, + sex <span class="hljs-keyword">string</span>, + age <span class="hljs-built_in">int</span>, + sibsp <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Siblings/Spouses Aboard</span> + parch <span class="hljs-built_in">int</span>, <span class="hljs-comment">-- Number of Parents/Children Aboard</span> + ticket <span class="hljs-keyword">string</span>, + fare <span class="hljs-keyword">double</span>, + cabin <span class="hljs-keyword">string</span>, + embarked <span class="hljs-keyword">string</span> ) -ROW FORMAT DELIMITED - FIELDS TERMINATED BY '|' - LINES TERMINATED BY '\n' -STORED AS TEXTFILE LOCATION '/dataset/titanic/test_raw'; - -hadoop fs -rm /dataset/titanic/test_raw/test.csv -awk '{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$3=substr($3,2,length($3)-2);print $0}' test.csv | hadoop fs -put - /dataset/titanic/test_raw/test.csv +<span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> + <span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'|'</span> + <span class="hljs-keyword">LINES</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'\n'</span> +<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE LOCATION <span class="hljs-string">'/dataset/titanic/test_raw'</span>; +</code></pre> +<pre><code class="lang-sh">hadoop fs -rm /dataset/titanic/<span class="hljs-built_in">test</span>_raw/test.csv +awk <span class="hljs-string">'{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|"; } NR >1 {$1=$1;$3=substr($3,2,length($3)-2);print $0}'</span> test.csv | hadoop fs -put - /dataset/titanic/<span class="hljs-built_in">test</span>_raw/test.csv </code></pre> <h2 id="data-preparation-for-randomforest">Data preparation for RandomForest</h2> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:output_row=<span class="hljs-literal">true</span>; @@ -2240,26 +2256,27 @@ awk '{ FPAT="([^,]*)|(\"[^\"]+\")";OFS="|&quo <p><code>Q</code> and <code>C</code> represent quantitative variable and categorical variables, respectively.</p> <p><em>Caution:</em> Note that the output of <code>guess_attribute_types</code> is not perfect. Revise it by your self. For example, <code>pclass</code> is a categorical variable.</p> -<pre><code class="lang-sql">set hivevar:attrs=C,C,C,Q,Q,Q,C,Q,C,C; - -drop table model_rf; -create table model_rf -AS -select - train_randomforest_classifier(features, survived, "-trees 500 -attrs ${attrs}") - -- as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests) -from +<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:attrs=C,C,C,Q,Q,Q,C,Q,C,C; + +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> model_rf; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> model_rf +<span class="hljs-keyword">AS</span> +<span class="hljs-keyword">select</span> + train_randomforest_classifier(features, survived, <span class="hljs-string">"-trees 500 -attrs ${attrs}"</span>) + <span class="hljs-comment">-- as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests)</span> +<span class="hljs-keyword">from</span> train_rf ; -select - array_sum(var_importance) as var_importance, - sum(oob_errors) / sum(oob_tests) as oob_err_rate -from +<span class="hljs-keyword">select</span> + array_sum(var_importance) <span class="hljs-keyword">as</span> var_importance, + <span class="hljs-keyword">sum</span>(oob_errors) / <span class="hljs-keyword">sum</span>(oob_tests) <span class="hljs-keyword">as</span> oob_err_rate +<span class="hljs-keyword">from</span> model_rf; - -> [137.00242639169272,1194.2140119834373,328.78017188176966,628.2568660509628,200.31275032394072,160.12876797647078,1083.5987543408116,664.1234312561456,422.89449844090393,130.72019667694784] 0.18742985409652077 </code></pre> +<blockquote> +<p>[137.00242639169272,1194.2140119834373,328.78017188176966,628.2568660509628,200.31275032394072,160.12876797647078,1083.5987543408116,664.1234312561456,422.89449844090393,130.72019667694784] 0.18742985409652077</p> +</blockquote> <h1 id="prediction">Prediction</h1> <pre><code class="lang-sql"><span class="hljs-keyword">SET</span> hivevar:classification=<span class="hljs-literal">true</span>; <span class="hljs-keyword">set</span> hive.<span class="hljs-keyword">auto</span>.<span class="hljs-keyword">convert</span>.<span class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>; @@ -2277,16 +2294,27 @@ from <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> passengerid, - rf_ensemble(predicted) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> t.passengerid, <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 or before</span> <span class="hljs-comment">-- tree_predict(p.model, t.features, ${classification}) as predicted</span> -　　 <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span> - tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span> + <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + p.model_weight, + tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, ${classification}) <span class="hljs-keyword">as</span> predicted <span class="hljs-keyword">FROM</span> ( - <span class="hljs-keyword">SELECT</span> model_id, model_type, pred_model <span class="hljs-keyword">FROM</span> model_rf + <span class="hljs-keyword">SELECT</span> + <span class="hljs-comment">-- model_id, pred_model</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + model_id, model_weight, <span class="hljs-keyword">model</span> + <span class="hljs-keyword">FROM</span> + model_rf <span class="hljs-keyword">DISTRIBUTE</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) ) p <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> test_rf t @@ -2309,94 +2337,137 @@ from <span class="hljs-keyword">ORDER</span> <span class="hljs-keyword">BY</span> passengerid <span class="hljs-keyword">ASC</span>; </code></pre> <pre><code class="lang-sh">hadoop fs -getmerge /user/hive/warehouse/titanic.db/predicted_rf_submit predicted_rf_submit.csv - sed -i <span class="hljs-_">-e</span> <span class="hljs-string">"1i PassengerId,Survived"</span> predicted_rf_submit.csv </code></pre> <p>Accuracy would gives <code>0.76555</code> for a Kaggle submission.</p> +<h1 id="graphvis-export">Graphvis export</h1> +<div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p><code>tree_export</code> feature is supported from Hivemall v0.5-rc.1 or later. +Better to limit tree depth on training by <code>-depth</code> option to plot a Decision Tree.</p></div></div> +<p>Hivemall provide <code>tree_export</code> to export a decision tree into <a href="http://www.graphviz.org/"; target="_blank">Graphviz</a> or human-readable Javascript format. You can find the usage by issuing the following query:</p> +<pre><code>> select tree_export("","-help"); + +usage: tree_export(string model, const string options, optional + array<string> featureNames=null, optional array<string> + classNames=null) - exports a Decision Tree model as javascript/dot] + [-help] [-output_name <arg>] [-r] [-t <arg>] + -help Show function help + -output_name,--outputName <arg> output name [default: predicted] + -r,--regression Is regression tree or not + -t,--type <arg> Type of output [default: js, + javascript/js, graphvis/dot +</code></pre><pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> model_exported + <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> ORC tblproperties(<span class="hljs-string">"orc.compress"</span>=<span class="hljs-string">"SNAPPY"</span>) +<span class="hljs-keyword">AS</span> +<span class="hljs-keyword">select</span> + model_id, + tree_export(<span class="hljs-keyword">model</span>, <span class="hljs-string">"-type javascript -output_name survived"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'pclass'</span>,<span class="hljs-string">'name'</span>,<span class="hljs-string">'sex'</span>,<span class="hljs-string">'age'</span>,<span class="hljs-string">'sibsp'</span>,<span class="hljs-string">'parch'</span>,<span class="hljs-string">'ticket'</span>,<span class="hljs-string">'fare'</span>,<span class="hljs-string">'cabin'</span>,<span class="hljs-string">'embarked'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'no'</span>,<span class="hljs-string">'yes'</span>)) <span class="hljs-keyword">as</span> js, + tree_export(<span class="hljs-keyword">model</span>, <span class="hljs-string">"-type graphvis -output_name survived"</span>, <span class="hljs-built_in">array</span>(<span class="hljs-string">'pclass'</span>,<span class="hljs-string">'name'</span>,<span class="hljs-string">'sex'</span>,<span class="hljs-string">'age'</span>,<span class="hljs-string">'sibsp'</span>,<span class="hljs-string">'parch'</span>,<span class="hljs-string">'ticket'</span>,<span class="hljs-string">'fare'</span>,<span class="hljs-string">'cabin'</span>,<span class="hljs-string">'embarked'</span>), <span class="hljs-built_in">array</span>(<span class="hljs-string">'no'</span>,<span class="hljs-string">'yes'</span>)) <span class="hljs-keyword">as</span> dot +<span class="hljs-keyword">from</span> + model_rf +<span class="hljs-comment">-- limit 1</span> +; +</code></pre> +<p><a href="https://gist.github.com/myui/a83ba3795bad9b278cf8bcc59f946e2c#file-titanic-dot"; target="_blank">Here is an example</a> plotting a decision tree using Graphvis or <a href="http://viz-js.com/"; target="_blank">Vis.js</a>.</p> <hr> <h1 id="test-by-dividing-training-dataset">Test by dividing training dataset</h1> -<pre><code class="lang-sql">drop table train_rf_07; -create table train_rf_07 -as -select * from train_rf -where rnd < 0.7; - -drop table test_rf_03; -create table test_rf_03 -as -select * from train_rf -where rnd >= 0.7; - -drop table model_rf_07; -create table model_rf_07 -AS -select - train_randomforest_classifier(features, survived, "-trees 500 -attrs ${attrs}") -from +<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> train_rf_07; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> train_rf_07 +<span class="hljs-keyword">as</span> +<span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> train_rf +<span class="hljs-keyword">where</span> rnd < <span class="hljs-number">0.7</span>; + +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> test_rf_03; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> test_rf_03 +<span class="hljs-keyword">as</span> +<span class="hljs-keyword">select</span> * <span class="hljs-keyword">from</span> train_rf +<span class="hljs-keyword">where</span> rnd >= <span class="hljs-number">0.7</span>; + +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> model_rf_07; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> model_rf_07 +<span class="hljs-keyword">AS</span> +<span class="hljs-keyword">select</span> + train_randomforest_classifier(features, survived, <span class="hljs-string">"-trees 500 -attrs ${attrs}"</span>) +<span class="hljs-keyword">from</span> train_rf_07; -select - array_sum(var_importance) as var_importance, - sum(oob_errors) / sum(oob_tests) as oob_err_rate -from +<span class="hljs-keyword">select</span> + array_sum(var_importance) <span class="hljs-keyword">as</span> var_importance, + <span class="hljs-keyword">sum</span>(oob_errors) / <span class="hljs-keyword">sum</span>(oob_tests) <span class="hljs-keyword">as</span> oob_err_rate +<span class="hljs-keyword">from</span> model_rf_07; -> [116.12055542977338,960.8569891444097,291.08765260103837,469.74671636586226,163.721292772701,120.784769882858,847.9769298113661,554.4617571355476,346.3500941757221,97.42593940113392] 0.1838351822503962 - -SET hivevar:classification=true; -SET hive.mapjoin.optimized.hashtable=false; -SET mapred.reduce.tasks=16; +</code></pre> +<blockquote> +<p>[116.12055542977338,960.8569891444097,291.08765260103837,469.74671636586226,163.721292772701,120.784769882858,847.9769298113661,554.4617571355476,346.3500941757221,97.42593940113392] 0.1838351822503962</p> +</blockquote> +<pre><code class="lang-sql"><span class="hljs-keyword">SET</span> hivevar:classification=<span class="hljs-literal">true</span>; +<span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span class="hljs-literal">false</span>; +<span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span class="hljs-number">16</span>; -drop table predicted_rf_03; -create table predicted_rf_03 -as -SELECT +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted_rf_03; +<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted_rf_03 +<span class="hljs-keyword">as</span> +<span class="hljs-keyword">SELECT</span> passengerid, predicted.label, predicted.probability, predicted.probabilities -FROM ( - SELECT +<span class="hljs-keyword">FROM</span> ( + <span class="hljs-keyword">SELECT</span> passengerid, - rf_ensemble(predicted) as predicted - FROM ( - SELECT + <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> + <span class="hljs-keyword">FROM</span> ( + <span class="hljs-keyword">SELECT</span> t.passengerid, - -- hivemall v0.4.1-alpha.2 or before - -- tree_predict(p.model, t.features, ${classification}) as predicted - -- hivemall v0.4.1-alpha.3 or later - tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted - FROM ( - SELECT model_id, model_type, pred_model FROM model_rf_07 - DISTRIBUTE BY rand(1) + <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 or before</span> + <span class="hljs-comment">-- tree_predict(p.model, t.features, ${classification}) as predicted</span> + <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span> + <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + p.model_weight, + tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, ${classification}) <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">FROM</span> ( + <span class="hljs-keyword">SELECT</span> + <span class="hljs-comment">-- model_id, model_type, pred_model</span> + <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + model_id, model_weight, <span class="hljs-keyword">model</span> + <span class="hljs-keyword">FROM</span> + model_rf_07 + <span class="hljs-keyword">DISTRIBUTE</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) ) p - LEFT OUTER JOIN test_rf_03 t + <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> test_rf_03 t ) t1 - group by + <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> passengerid ) t2 ; -create or replace view rf_submit_03 as -select - t.survived as actual, - p.label as predicted, +<span class="hljs-keyword">create</span> <span class="hljs-keyword">or</span> <span class="hljs-keyword">replace</span> <span class="hljs-keyword">view</span> rf_submit_03 <span class="hljs-keyword">as</span> +<span class="hljs-keyword">select</span> + t.survived <span class="hljs-keyword">as</span> actual, + p.label <span class="hljs-keyword">as</span> predicted, p.probabilities -from +<span class="hljs-keyword">from</span> test_rf_03 t - JOIN predicted_rf_03 p on (t.passengerid = p.passengerid) + <span class="hljs-keyword">JOIN</span> predicted_rf_03 p <span class="hljs-keyword">on</span> (t.passengerid = p.passengerid) ; -select count(1) from test_rf_03; -> 260 - -set hivevar:testcnt=260; - -select count(1)/${testcnt} as accuracy -from rf_submit_03 -where actual = predicted; +<span class="hljs-keyword">select</span> <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">from</span> test_rf_03; +</code></pre> +<blockquote> +<p>260</p> +</blockquote> +<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:testcnt=<span class="hljs-number">260</span>; -> 0.8 +<span class="hljs-keyword">select</span> <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>)/${testcnt} <span class="hljs-keyword">as</span> accuracy +<span class="hljs-keyword">from</span> rf_submit_03 +<span class="hljs-keyword">where</span> actual = predicted; </code></pre> +<blockquote> +<p>0.8153846153846154</p> +</blockquote> <p><div id="page-footer" class="localized-footer"><hr><!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. 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