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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/clustering/plsa.html ---------------------------------------------------------------------- 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. 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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/docker/getting_started.html ---------------------------------------------------------------------- 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 <script> var gitbook = gitbook || []; 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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/eval/auc.html ---------------------------------------------------------------------- diff --git a/userguide/eval/auc.html b/userguide/eval/auc.html index 5d5f349..72eba78 100644 --- a/userguide/eval/auc.html +++ b/userguide/eval/auc.html @@ -97,10 +97,10 @@ <link rel="shortcut icon" href="../gitbook/images/favicon.ico" type="image/x-icon"> - <link rel="next" href="rank.html" /> + <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. 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