<?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>2306-2495</journal-id>
<journal-title><![CDATA[Serie Científica de la Universidad de las Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Serie Científica]]></abbrev-journal-title>
<issn>2306-2495</issn>
<publisher>
<publisher-name><![CDATA[Universidad de las Ciencias Informáticas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2306-24952025000100283</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis integral de algoritmos de clasificación en aprendizaje automático: perspectivas, comparaciones y aplicaciones]]></article-title>
<article-title xml:lang="en"><![CDATA[Comprehensive analysis of classification algorithms in machine learning: perspectives, comparisons and applications]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romero Ibarra]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Superior Tecnológico ARGOS  ]]></institution>
<addr-line><![CDATA[ Guayaquil]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<volume>18</volume>
<numero>1</numero>
<fpage>283</fpage>
<lpage>304</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2306-24952025000100283&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2306-24952025000100283&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2306-24952025000100283&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La evaluación de algoritmos de clasificación en aprendizaje automático es crucial para garantizar un desempeño óptimo en diversas aplicaciones. Esta revisión bibliográfica analizó métricas y estrategias para evaluar la efectividad de los modelos clasificadores. Se examinaron métricas básicas como precisión, sensibilidad y especificidad, que miden el desempeño en términos de predicciones correctas para clases positivas y negativas. También se abordó el valor F1, que combina precisión y sensibilidad para proporcionar un equilibrio entre ambas. Se exploraron métricas avanzadas, como el área bajo la curva ROC, que evalúa la capacidad del modelo para clasificar correctamente mediante diferentes umbrales de decisión. Además, se destacó la matriz de confusión, que ofrece una representación detallada de las predicciones correctas e incorrectas del modelo. La revisión también consideró criterios de inclusión y exclusión para seleccionar investigaciones relevantes sobre algoritmos de clasificación. Estos criterios garantizan la calidad y relevancia de los estudios, permitiendo una evaluación robusta y comprensiva de enfoques y técnicas en el campo. Esta revisión proporciona una visión detallada de las métricas de evaluación para algoritmos de clasificación y resalta aspectos clave al buscar y analizar estudios relevantes. Este análisis es fundamental para comprender y aplicar de manera efectiva estos algoritmos en diversos contextos y aplicaciones prácticas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Evaluating classification algorithms in machine learning is crucial to ensure optimal performance in various applications. This literature review analyzed metrics and strategies to evaluate the effectiveness of classifier models. Basic metrics such as accuracy, sensitivity, and specificity, which measure performance in terms of correct predictions for positive and negative classes, were examined. The F1 value, which combines accuracy and sensitivity to provide a balance between the two, was also addressed. Advanced metrics such as the area under the ROC curve, which assesses the model's ability to correctly classify using different decision thresholds, were explored. Additionally, the confusion matrix, which provides a detailed representation of the correct and incorrect predictions of the model, was highlighted. The review also considered inclusion and exclusion criteria to select relevant research on classification algorithms. These criteria ensure the quality and relevance of studies, allowing for a robust and comprehensive evaluation of approaches and techniques in the field. This review provides a detailed overview of evaluation metrics for classification algorithms and highlights key aspects when searching and analyzing relevant studies. This analysis is essential to effectively understand and apply these algorithms in various contexts and practical applications.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[algoritmos de clasificación]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[métricas de evaluación]]></kwd>
<kwd lng="es"><![CDATA[rendimiento del modelo]]></kwd>
<kwd lng="es"><![CDATA[validación de modelos]]></kwd>
<kwd lng="en"><![CDATA[Classification algorithms]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[evaluation metrics]]></kwd>
<kwd lng="en"><![CDATA[model performance]]></kwd>
<kwd lng="en"><![CDATA[model validation]]></kwd>
</kwd-group>
</article-meta>
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