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Posted to commits@hivemall.apache.org by my...@apache.org on 2017/09/13 14:10:32 UTC
[20/23] incubator-hivemall-site git commit: Updated userguide for
evaluation section
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/binaryclass/webspam_dataset.html
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diff --git a/userguide/binaryclass/webspam_dataset.html b/userguide/binaryclass/webspam_dataset.html
index e98a2d1..7523ec6 100644
--- a/userguide/binaryclass/webspam_dataset.html
+++ b/userguide/binaryclass/webspam_dataset.html
@@ -244,7 +244,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -707,14 +707,14 @@
- <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
- <a href="../eval/stat_eval.html">
+ <a href="../eval/binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -743,13 +743,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+ <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
- <a href="../eval/rank.html">
+ <a href="../eval/multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+
+ <a href="../eval/regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+
+ <a href="../eval/rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -758,12 +788,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+ <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
<a href="../eval/datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -774,12 +804,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
<a href="../eval/lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2199,7 +2229,7 @@ CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</sp
<span class="hljs-keyword">select</span>
s.<span class="hljs-keyword">rowid</span>,
label,
- addBias(features) <span class="hljs-keyword">as</span> features
+ add_bias(features) <span class="hljs-keyword">as</span> features
<span class="hljs-keyword">from</span> webspam_raw s
<span class="hljs-keyword">where</span> <span class="hljs-keyword">not</span> <span class="hljs-keyword">exists</span> (<span class="hljs-keyword">select</span> <span class="hljs-keyword">rowid</span> <span class="hljs-keyword">from</span> webspam_test t <span class="hljs-keyword">where</span> s.<span class="hljs-keyword">rowid</span> = t.<span class="hljs-keyword">rowid</span>)
CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">43</span>);
@@ -2222,10 +2252,10 @@ CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</sp
<span class="hljs-keyword">split</span>(feature,<span class="hljs-string">":"</span>)[<span class="hljs-number">0</span>] <span class="hljs-keyword">as</span> feature,
<span class="hljs-keyword">cast</span>(<span class="hljs-keyword">split</span>(feature,<span class="hljs-string">":"</span>)[<span class="hljs-number">1</span>] <span class="hljs-keyword">as</span> <span class="hljs-built_in">float</span>) <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span>
<span class="hljs-keyword">from</span>
- webspam_test LATERAL <span class="hljs-keyword">VIEW</span> explode(addBias(features)) t <span class="hljs-keyword">AS</span> feature;
+ webspam_test LATERAL <span class="hljs-keyword">VIEW</span> explode(add_bias(features)) t <span class="hljs-keyword">AS</span> feature;
</code></pre>
-<p><em>Caution:</em> For this dataset, use small <em>shufflebuffersize</em> because each training example has lots of features though (xtimes <em> shufflebuffersize </em> N) training examples are cached in memory.
-<div id="page-footer" class="localized-footer"><hr><!--
+<p><em>Caution:</em> For this dataset, use small <em>shufflebuffersize</em> because each training example has lots of features though (xtimes <em> shufflebuffersize </em> N) training examples are cached in memory.</p>
+<p><div id="page-footer" class="localized-footer"><hr><!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
@@ -2280,7 +2310,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
<script>
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/binaryclass/webspam_scw.html
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diff --git a/userguide/binaryclass/webspam_scw.html b/userguide/binaryclass/webspam_scw.html
index ba19fa8..5a9441f 100644
--- a/userguide/binaryclass/webspam_scw.html
+++ b/userguide/binaryclass/webspam_scw.html
@@ -244,7 +244,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -707,14 +707,14 @@
- <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
- <a href="../eval/stat_eval.html">
+ <a href="../eval/binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -743,13 +743,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+ <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
- <a href="../eval/rank.html">
+ <a href="../eval/multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+
+ <a href="../eval/regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+
+ <a href="../eval/rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -758,12 +788,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+ <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
<a href="../eval/datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -774,12 +804,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
<a href="../eval/lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2340,7 +2370,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
<script>
var gitbook = gitbook || [];
gitbook.push(function() {
- gitbook.page.hasChanged({"page":{"title":"PA1, AROW, SCW","level":"6.6.2","depth":2,"next":{"title":"Kaggle Titanic tutorial","level":"6.7","depth":1,"path":"binaryclass/titanic_rf.md","ref":"binaryclass/titanic_rf.md","articles":[]},"previous":{"title":"Data pareparation","level":"6.6.1","depth":2,"path":"binaryclass/webspam_dataset.md","ref":"binaryclass/webspam_dataset.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hive
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/clustering/lda.html
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diff --git a/userguide/clustering/lda.html b/userguide/clustering/lda.html
index d8fc133..68afdb6 100644
--- a/userguide/clustering/lda.html
+++ b/userguide/clustering/lda.html
@@ -244,7 +244,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -707,14 +707,14 @@
- <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
- <a href="../eval/stat_eval.html">
+ <a href="../eval/binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -743,13 +743,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+ <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
- <a href="../eval/rank.html">
+ <a href="../eval/multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+
+ <a href="../eval/regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+
+ <a href="../eval/rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -758,12 +788,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+ <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
<a href="../eval/datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -774,12 +804,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
<a href="../eval/lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2504,7 +2534,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
<script>
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diff --git a/userguide/clustering/plsa.html b/userguide/clustering/plsa.html
index 0d6d392..45c8af4 100644
--- a/userguide/clustering/plsa.html
+++ b/userguide/clustering/plsa.html
@@ -244,7 +244,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -707,14 +707,14 @@
- <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
- <a href="../eval/stat_eval.html">
+ <a href="../eval/binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -743,13 +743,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+ <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
- <a href="../eval/rank.html">
+ <a href="../eval/multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+
+ <a href="../eval/regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+
+ <a href="../eval/rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -758,12 +788,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+ <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
<a href="../eval/datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -774,12 +804,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
<a href="../eval/lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2403,8 +2433,8 @@ topic <span class="hljs-keyword">as</span> (
<p>For instance, <a href="http://qwone.com/~jason/20Newsgroups/" target="_blank">20 newsgroups dataset</a> which consists of 10906 realistic documents empirically requires the following options:</p>
<pre><code class="lang-sql"><span class="hljs-keyword">SELECT</span> train_plsa(features, <span class="hljs-string">'-topics 20 -iter 10 -s 128 -delta 0.01 -alpha 512 -eps 0.1'</span>)
</code></pre>
-<p>Clearly, <code>alpha</code> is much larger than <code>0.01</code> which was used for the dummy data above. Let you keep in mind that an appropriate value of <code>alpha</code> highly depends on the number of documents and mini-batch size.
-<div id="page-footer" class="localized-footer"><hr><!--
+<p>Clearly, <code>alpha</code> is much larger than <code>0.01</code> which was used for the dummy data above. Let you keep in mind that an appropriate value of <code>alpha</code> highly depends on the number of documents and mini-batch size.</p>
+<p><div id="page-footer" class="localized-footer"><hr><!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
@@ -2459,7 +2489,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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diff --git a/userguide/docker/getting_started.html b/userguide/docker/getting_started.html
index 694e98a..03a064f 100644
--- a/userguide/docker/getting_started.html
+++ b/userguide/docker/getting_started.html
@@ -242,7 +242,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -705,14 +705,14 @@
- <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
- <a href="../eval/stat_eval.html">
+ <a href="../eval/binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -741,13 +741,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+ <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
- <a href="../eval/rank.html">
+ <a href="../eval/multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+
+ <a href="../eval/regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+
+ <a href="../eval/rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -756,12 +786,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+ <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
<a href="../eval/datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -772,12 +802,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
<a href="../eval/lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2301,7 +2331,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/eval/auc.html
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diff --git a/userguide/eval/auc.html b/userguide/eval/auc.html
index 5d5f349..72eba78 100644
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@@ -97,10 +97,10 @@
<link rel="shortcut icon" href="../gitbook/images/favicon.ico" type="image/x-icon">
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+ <link rel="next" href="multilabel_classification_measures.html" />
- <link rel="prev" href="stat_eval.html" />
+ <link rel="prev" href="binary_classification_measures.html" />
</head>
@@ -244,7 +244,7 @@
<b>1.3.1.</b>
- Explicit addBias() for better prediction
+ Explicit add_bias() for better prediction
</a>
@@ -707,14 +707,14 @@
- <li class="chapter " data-level="4.1" data-path="stat_eval.html">
+ <li class="chapter " data-level="4.1" data-path="binary_classification_measures.html">
- <a href="stat_eval.html">
+ <a href="binary_classification_measures.html">
<b>4.1.</b>
- Statistical evaluation of a prediction model
+ Binary Classification Metrics
</a>
@@ -743,13 +743,43 @@
</li>
- <li class="chapter " data-level="4.2" data-path="rank.html">
+ <li class="chapter " data-level="4.2" data-path="multilabel_classification_measures.html">
- <a href="rank.html">
+ <a href="multilabel_classification_measures.html">
<b>4.2.</b>
+ Multi-label Classification Metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.3" data-path="regression.html">
+
+ <a href="regression.html">
+
+
+ <b>4.3.</b>
+
+ Regression metrics
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="4.4" data-path="rank.html">
+
+ <a href="rank.html">
+
+
+ <b>4.4.</b>
+
Ranking Measures
</a>
@@ -758,12 +788,12 @@
</li>
- <li class="chapter " data-level="4.3" data-path="datagen.html">
+ <li class="chapter " data-level="4.5" data-path="datagen.html">
<a href="datagen.html">
- <b>4.3.</b>
+ <b>4.5.</b>
Data Generation
@@ -774,12 +804,12 @@
<ul class="articles">
- <li class="chapter " data-level="4.3.1" data-path="lr_datagen.html">
+ <li class="chapter " data-level="4.5.1" data-path="lr_datagen.html">
<a href="lr_datagen.html">
- <b>4.3.1.</b>
+ <b>4.5.1.</b>
Logistic Regression data generation
@@ -2218,7 +2248,7 @@
<span class="hljs-keyword">union</span> all
<span class="hljs-keyword">select</span> <span class="hljs-number">0.7</span> <span class="hljs-keyword">as</span> prob, <span class="hljs-number">1</span> <span class="hljs-keyword">as</span> label
)
-<span class="hljs-keyword">select</span>
+<span class="hljs-keyword">select</span>
auc(prob, label) <span class="hljs-keyword">as</span> auc
<span class="hljs-keyword">from</span> (
<span class="hljs-keyword">select</span> prob, label
@@ -2229,7 +2259,7 @@
<p>This query returns <code>0.83333</code> as AUC.</p>
<p>Since AUC is a metric based on ranked probability-label pairs as mentioned above, input data (rows) needs to be ordered by scores in a descending order.</p>
<h2 id="parallel-approximate-auc-computation">Parallel approximate AUC computation</h2>
-<p>Meanwhile, Hive's <code>distribute by</code> clause allows you to compute AUC in parallel: </p>
+<p>Meanwhile, Hive's <code>distribute by</code> clause allows you to compute AUC in parallel:</p>
<pre><code class="lang-sql">with data as (
<span class="hljs-keyword">select</span> <span class="hljs-number">0.5</span> <span class="hljs-keyword">as</span> prob, <span class="hljs-number">0</span> <span class="hljs-keyword">as</span> label
<span class="hljs-keyword">union</span> all
@@ -2241,7 +2271,7 @@
<span class="hljs-keyword">union</span> all
<span class="hljs-keyword">select</span> <span class="hljs-number">0.7</span> <span class="hljs-keyword">as</span> prob, <span class="hljs-number">1</span> <span class="hljs-keyword">as</span> label
)
-<span class="hljs-keyword">select</span>
+<span class="hljs-keyword">select</span>
auc(prob, label) <span class="hljs-keyword">as</span> auc
<span class="hljs-keyword">from</span> (
<span class="hljs-keyword">select</span> prob, label
@@ -2252,7 +2282,7 @@
</code></pre>
<p>Note that <code>floor(prob / 0.2)</code> means that the rows are distributed to 5 bins for the AUC computation because the column <code>prob</code> is in a [0, 1] range.</p>
<h1 id="difference-between-auc-and-logarithmic-loss">Difference between AUC and Logarithmic Loss</h1>
-<p>Hivemall has another metric called <a href="stat_eval.html#logarithmic-loss">Logarithmic Loss</a> for binary classification. Both AUC and Logarithmic Loss compute scores for probability-label pairs. </p>
+<p>Hivemall has another metric called <a href="regression.html#logarithmic-loss">Logarithmic Loss</a> for binary classification. Both AUC and Logarithmic Loss compute scores for probability-label pairs.</p>
<p>Score produced by AUC is a relative metric based on sorted pairs. On the other hand, Logarithmic Loss simply gives a metric by comparing probability with its truth label one-by-one.</p>
<p>To give an example, <code>auc(prob, label)</code> and <code>logloss(prob, label)</code> respectively returns <code>0.83333</code> and <code>0.54001</code> in the above case. Note that larger AUC and smaller Logarithmic Loss are better.</p>
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