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         <div class="pull-left"><div id="bannerLeft"><h2>Apache Hivemall</h2>
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         <li id="publishDate">Last Published: 2019-10-31<span class="divider">|</span>
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 <!--
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 --><h1>Apache Hivemall Overview</h1>
 <p>Apache Hivemall is a scalable machine learning library that runs on Apache Hive/Pig/Spark. Apache Hivemall is designed to be scalable to the number of training instances as well as the number of training features.</p>
 <div class="section">
 <h2><a name="Supported_Algorithms"></a>Supported Algorithms</h2>
 <p>Apache Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive. </p></div>
 <div class="section">
 <h2><a name="Binary_Classification"></a>Binary Classification</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Perceptron">Perceptron</a></p></li>

 <li>
 <p>Passive Aggressive (PA, PA1, PA2)</p></li>

 <li>
 <p>Confidence Weighted (CW)</p></li>

 <li>
 <p>Adaptive Regularization of Weight Vectors (AROW)</p></li>

 <li>
 <p>Soft Confidence Weighted (SCW1, SCW2)</p></li>

 <li>
 <p>AdaGradRDA (w/ hinge loss)</p></li>

 <li>
 <p>Factorization Machine (w/ logistic loss)</p></li>
 </ul>
 <p><i>My recommendation is AROW, SCW1, AdaGradRDA, and Factorization Machine while it depends.</i></p></div>
 <div class="section">
 <h2><a name="Multi-class_Classification"></a>Multi-class Classification</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Perceptron">Perceptron</a></p></li>

 <li>
 <p>Passive Aggressive (PA, PA1, PA2)</p></li>

 <li>
 <p>Confidence Weighted (CW)</p></li>

 <li>
 <p>Adaptive Regularization of Weight Vectors (AROW)</p></li>

 <li>
 <p>Soft Confidence Weighted (SCW1, SCW2)</p></li>

 <li>
 <p>Random Forest Classifier</p></li>

 <li>
 <p>Gradient Tree Boosting (<i>Experimental</i>)</p></li>
 </ul>
 <p><i>My recommendation is AROW and SCW while it depends.</i></p></div>
 <div class="section">
 <h2><a name="Regression"></a>Regression</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Logistic_regression">Logistic Regression</a> using <a class="externalLink" href="http://en.wikipedia.org/wiki/Stochastic_gradient_descent">Stochastic Gradient Descent</a></p></li>

 <li>
 <p>AdaGrad, AdaDelta (w/ logistic Loss)</p></li>

 <li>
 <p>Passive Aggressive Regression (PA1, PA2)</p></li>

 <li>
 <p>AROW regression</p></li>

 <li>
 <p>Random Forest Regressor</p></li>

 <li>
 <p>Factorization Machine (w/ squared loss)</p></li>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Polynomial_regression">Polynomial Regression</a></p></li>
 </ul>
 <p><i>My recommendation for is AROW regression, AdaDelta, and Factorization Machine while it depends.</i></p></div>
 <div class="section">
 <h2><a name="Recommendation"></a>Recommendation</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/MinHash">Minhash</a> (<a class="externalLink" href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing">LSH</a> with jaccard index)</p></li>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Matrix_decomposition">Matrix Factorization</a> (sgd, adagrad)</p></li>

 <li>
 <p>Factorization Machine (squared loss for rating prediction)</p></li>
 </ul></div>
 <div class="section">
 <h2><a name="k-Nearest_Neighbor"></a>k-Nearest Neighbor</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/MinHash">Minhash</a> (<a class="externalLink" href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing">LSH</a> with jaccard index)</p></li>

 <li>
 <p>b-Bit minhash</p></li>

 <li>
 <p>Brute-force search using Cosine similarity</p></li>
 </ul></div>
 <div class="section">
 <h2><a name="Anomaly_Detection"></a>Anomaly Detection</h2>

 <ul>

 <li><a class="externalLink" href="http://en.wikipedia.org/wiki/Local_outlier_factor">Local Outlier Factor (LOF)</a></li>
 </ul></div>
 <div class="section">
 <h2><a name="Natural_Language_Processing"></a>Natural Language Processing</h2>

 <ul>

 <li>English/Japanese Text Tokenizer</li>
 </ul></div>
 <div class="section">
 <h2><a name="Feature_engineering"></a>Feature engineering</h2>

 <ul>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Feature_hashing">Feature Hashing</a> (MurmurHash, SHA1)</p></li>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Feature_scaling">Feature scaling</a> (Min-Max Normalization, Z-Score)</p></li>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Polynomial_kernel">Polynomial Features</a></p></li>

 <li>
 <p>Feature instances amplifier that reduces iterations on training</p></li>

 <li>
 <p><a class="externalLink" href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf">TF-IDF</a> vectorizer</p></li>

 <li>
 <p>Bias clause</p></li>

 <li>
 <p>Data generator for one-vs-the-rest classifiers</p></li>
 </ul></div>
 <div class="section">
 <h2><a name="System_requirements"></a>System requirements</h2>

 <ul>

 <li>
 <p>Hive 0.13 or later</p></li>

 <li>
 <p>Java 7 or later</p></li>

 <li>
 <p>Spark 2.1 or later for Apache Hivemall on Spark</p></li>

 <li>
 <p>Pig 0.15 or later for Apache Hivemall on Pig</p></li>
 </ul>
 <p>More detail in <a class="externalLink" href="http://hivemall-docs.readthedocs.io/en/latest/">documentation</a>.</p></div>
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                 Apache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by <a href="http://incubator.apache.org/">the Apache Incubator</a>.
                 Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications,
                 and decision making process have stabilized in a manner consistent with other successful ASF projects.
                 While incubation status is not necessarily a reflection of the completeness or stability of the code,
                 it does indicate that the project has yet to be fully endorsed by the ASF.
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	<li id="publishDate">Last Published: 2019-10-31<span class="divider">\|</span>
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	<li id="projectVersion">Version: 0.6.0-incubating-SNAPSHOT</li>
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	<!--
	Licensed to the Apache Software Foundation (ASF) under one
	or more contributor license agreements. See the NOTICE file
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	regarding copyright ownership. The ASF licenses this file
	to you under the Apache License, Version 2.0 (the
	"License"); you may not use this file except in compliance
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	specific language governing permissions and limitations
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	--><h1>Apache Hivemall Overview</h1>
	<p>Apache Hivemall is a scalable machine learning library that runs on Apache Hive/Pig/Spark. Apache Hivemall is designed to be scalable to the number of training instances as well as the number of training features.</p>
	<div class="section">
	<h2><a name="Supported_Algorithms"></a>Supported Algorithms</h2>
	<p>Apache Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive. </p></div>
	<div class="section">
	<h2><a name="Binary_Classification"></a>Binary Classification</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Perceptron">Perceptron</a></p></li>

	<li>
	<p>Passive Aggressive (PA, PA1, PA2)</p></li>

	<li>
	<p>Confidence Weighted (CW)</p></li>

	<li>
	<p>Adaptive Regularization of Weight Vectors (AROW)</p></li>

	<li>
	<p>Soft Confidence Weighted (SCW1, SCW2)</p></li>

	<li>
	<p>AdaGradRDA (w/ hinge loss)</p></li>

	<li>
	<p>Factorization Machine (w/ logistic loss)</p></li>
	</ul>
	<p><i>My recommendation is AROW, SCW1, AdaGradRDA, and Factorization Machine while it depends.</i></p></div>
	<div class="section">
	<h2><a name="Multi-class_Classification"></a>Multi-class Classification</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Perceptron">Perceptron</a></p></li>

	<li>
	<p>Passive Aggressive (PA, PA1, PA2)</p></li>

	<li>
	<p>Confidence Weighted (CW)</p></li>

	<li>
	<p>Adaptive Regularization of Weight Vectors (AROW)</p></li>

	<li>
	<p>Soft Confidence Weighted (SCW1, SCW2)</p></li>

	<li>
	<p>Random Forest Classifier</p></li>

	<li>
	<p>Gradient Tree Boosting (<i>Experimental</i>)</p></li>
	</ul>
	<p><i>My recommendation is AROW and SCW while it depends.</i></p></div>
	<div class="section">
	<h2><a name="Regression"></a>Regression</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Logistic_regression">Logistic Regression</a> using <a class="externalLink" href="http://en.wikipedia.org/wiki/Stochastic_gradient_descent">Stochastic Gradient Descent</a></p></li>

	<li>
	<p>AdaGrad, AdaDelta (w/ logistic Loss)</p></li>

	<li>
	<p>Passive Aggressive Regression (PA1, PA2)</p></li>

	<li>
	<p>AROW regression</p></li>

	<li>
	<p>Random Forest Regressor</p></li>

	<li>
	<p>Factorization Machine (w/ squared loss)</p></li>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Polynomial_regression">Polynomial Regression</a></p></li>
	</ul>
	<p><i>My recommendation for is AROW regression, AdaDelta, and Factorization Machine while it depends.</i></p></div>
	<div class="section">
	<h2><a name="Recommendation"></a>Recommendation</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/MinHash">Minhash</a> (<a class="externalLink" href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing">LSH</a> with jaccard index)</p></li>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Matrix_decomposition">Matrix Factorization</a> (sgd, adagrad)</p></li>

	<li>
	<p>Factorization Machine (squared loss for rating prediction)</p></li>
	</ul></div>
	<div class="section">
	<h2><a name="k-Nearest_Neighbor"></a>k-Nearest Neighbor</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/MinHash">Minhash</a> (<a class="externalLink" href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing">LSH</a> with jaccard index)</p></li>

	<li>
	<p>b-Bit minhash</p></li>

	<li>
	<p>Brute-force search using Cosine similarity</p></li>
	</ul></div>
	<div class="section">
	<h2><a name="Anomaly_Detection"></a>Anomaly Detection</h2>

	<ul>

	<li><a class="externalLink" href="http://en.wikipedia.org/wiki/Local_outlier_factor">Local Outlier Factor (LOF)</a></li>
	</ul></div>
	<div class="section">
	<h2><a name="Natural_Language_Processing"></a>Natural Language Processing</h2>

	<ul>

	<li>English/Japanese Text Tokenizer</li>
	</ul></div>
	<div class="section">
	<h2><a name="Feature_engineering"></a>Feature engineering</h2>

	<ul>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Feature_hashing">Feature Hashing</a> (MurmurHash, SHA1)</p></li>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Feature_scaling">Feature scaling</a> (Min-Max Normalization, Z-Score)</p></li>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Polynomial_kernel">Polynomial Features</a></p></li>

	<li>
	<p>Feature instances amplifier that reduces iterations on training</p></li>

	<li>
	<p><a class="externalLink" href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf">TF-IDF</a> vectorizer</p></li>

	<li>
	<p>Bias clause</p></li>

	<li>
	<p>Data generator for one-vs-the-rest classifiers</p></li>
	</ul></div>
	<div class="section">
	<h2><a name="System_requirements"></a>System requirements</h2>

	<ul>

	<li>
	<p>Hive 0.13 or later</p></li>

	<li>
	<p>Java 7 or later</p></li>

	<li>
	<p>Spark 2.1 or later for Apache Hivemall on Spark</p></li>

	<li>
	<p>Pig 0.15 or later for Apache Hivemall on Pig</p></li>
	</ul>
	<p>More detail in <a class="externalLink" href="http://hivemall-docs.readthedocs.io/en/latest/">documentation</a>.</p></div>
	</div>
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	and decision making process have stabilized in a manner consistent with other successful ASF projects.
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