<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2222-145X</journal-id>
<journal-title><![CDATA[Reportes científicos de la FACEN]]></journal-title>
<abbrev-journal-title><![CDATA[Rep. cient. FACEN]]></abbrev-journal-title>
<issn>2222-145X</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ciencias Exactas y Naturales]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2222-145X2022000100073</article-id>
<article-id pub-id-type="doi">10.18004/rcfacen.2022.13.1.73</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación de exactitud de Naive Bayes y Regresión Logística para clasificación con atributos y clases binarios]]></article-title>
<article-title xml:lang="en"><![CDATA[Accuracy evaluation of Naive Bayes and Logistic Regression for classification with binary attributes and classes]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Pezoa]]></surname>
<given-names><![CDATA[Edgar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cáceres-Estigarribia]]></surname>
<given-names><![CDATA[Antoliano]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Grillo]]></surname>
<given-names><![CDATA[Sebastián Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera]]></surname>
<given-names><![CDATA[Edher]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Asunción Facultad de Ciencias Exactas y Naturales Departamento de Matemática]]></institution>
<addr-line><![CDATA[San Lorenzo ]]></addr-line>
<country>Paraguay</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Asunción Facultad de Ciencias y Tecnologías ]]></institution>
<addr-line><![CDATA[Asunción ]]></addr-line>
<country>Paraguay</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Asunción Facultad de Ciencias Exactas y Naturales Laboratorio de Análisis Molecular y Elemental]]></institution>
<addr-line><![CDATA[San Lorenzo ]]></addr-line>
<country>Paraguay</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Nacional de Asunción Facultad Politécnica Departamento de Ciencias Básicas]]></institution>
<addr-line><![CDATA[San Lorenzo ]]></addr-line>
<country>Paraguay</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Nacional de Asunción Facultad Politécnica Núcleo de Investigación y Desarrollo Tecnológico (NIDTEC)]]></institution>
<addr-line><![CDATA[San Lorenzo ]]></addr-line>
<country>Paraguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>13</volume>
<numero>1</numero>
<fpage>73</fpage>
<lpage>84</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_arttext&amp;pid=S2222-145X2022000100073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_abstract&amp;pid=S2222-145X2022000100073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_pdf&amp;pid=S2222-145X2022000100073&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: En ciencia de los datos, la mayoría de los modelos de clasificación están en la categoría de modelos discriminativos o de modelos generativos. Los modelos discriminativos solamente capturan la relación entre los atributos de una instancia y su clase, mientras que los modelos generativos buscan representar toda la distribución de datos. Aunque la mayoría de los modelos de clasificación sean discriminativos, no se puede asegurar que este tipo de modelos sea mejor que los modelos generativos. En ese sentido, se aborda la comparación de los algoritmos Naive Bayes y Regresión Logística como modelos muy representativos de los clasificadores discriminativos y generativos, respectivamente. En este trabajo son evaluadas la exactitud de los modelos de Naive Bayes y Regresión Logística en función al número de atributos e instancias de un conjunto de datos artificiales, donde tanto los atributos como las clases son binarios. A diferencia de otras metodologías que emplean los conjuntos de datos para aproximar el error de clasificación, este trabajo solo emplea los conjuntos de datos para realizar el entrenamiento de los modelos, mientras que el error de clasificación es calculado de forma exacta para la distribución de los datos. Los experimentos muestran una exactitud de clasificación binaria que tiende a ser levemente mejor para la Regresión Logística usando 50 a 500 instancias de entrenamiento, cuando promediamos los resultados de distribuciones generadas aleatoriamente con 1 a 6 atributos binarios.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: In data science, most classification models fall into the category of either discriminative models or generative models. Discriminative models only capture the relationship between the attributes of an instance and its class, whereas generative models seek to represent the entire data distribution. Although most classification models are discriminative, it cannot be assured that this type of models is better than generative models. In that sense, the comparison of Naive Bayes and Logistic Regression algorithms as very representative models of dis- criminative and generative classifiers, respectively, is addressed. In this work, the accuracy of Naive Bayes and Logistic Regression models are evaluated as a function of the number of attributes and instances of an artificial dataset, where both attributes and classes are binary. Unlike other methodologies that employ the datasets to approximate the classification error, this work only employs the datasets to perform the training of the models, while the classification error is computed exactly for the distribution of the data. Experiments show a binary classification accuracy that tends to be slightly better for Logistic Regression using 50 to 500 training instances, when we average the results of randomly generated distributions with 1 to 6 binary attributes.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Naive Bayes]]></kwd>
<kwd lng="es"><![CDATA[regresión logística]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje supervisado]]></kwd>
<kwd lng="en"><![CDATA[Naive Bayes]]></kwd>
<kwd lng="en"><![CDATA[logistic regression]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[supervised learning]]></kwd>
</kwd-group>
</article-meta>
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