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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-fc89cbd7c2","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-8869cefdb1","text":"\u003Cp\u003EAl igual que las personas valoran distintas alternativas antes de tomar una decisión, los modelos de aprendizaje automático (ML) emplean diversos métodos para generar una \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-modeling/\"\u003Epredicción\u003C/a\u003E o recomendación. Los árboles de decisión son una opción muy extendida en ML porque descomponen los problemas en pasos claros, lo que facilita la interpretación de los resultados.\u003C/p\u003E\r\n\u003Cp\u003ESe utilizan habitualmente en el aprendizaje supervisado, donde los modelos aprenden a partir de ejemplos con respuestas correctas ya conocidas. Suelen aplicarse a tareas de clasificación, como la detección de correos electrónicos no deseados, y a tareas de regresión, como la previsión del consumo energético de un edificio. Lo que los diferencia es que su proceso de razonamiento puede visualizarse e interpretarse con claridad. Al observar cómo cada “rama” se bifurca en distintas direcciones para responder a preguntas basadas en datos, se entiende con precisión cómo el modelo ha llegado a un resultado concreto.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-9963abec0e","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es un árbol de decisión?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1e2371e81b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELos árboles de decisión funcionan como diagramas de flujo. Cada división representa un punto de decisión que conduce a resultados distintos. Esto permite tanto a personas como a sistemas analizar opciones, evaluar escenarios y comprender fácilmente los resultados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-b5acb3b0bb","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Cómo funciona un árbol de decisión? "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-eea54426ef","text":"\u003Cp\u003EUn árbol de decisión descompone un problema en una secuencia de preguntas. Cada pregunta reduce la incertidumbre hasta que la respuesta se vuelve evidente.\u003C/p\u003E\n\u003Cp\u003EEl proceso comienza en la raíz, con una pregunta fundamentada en los datos. En este punto, el algoritmo realiza la selección de características, identificando la variable más relevante para dividir los datos. Cada respuesta conduce a una nueva pregunta, nuevamente basada en la característica que mejor separa los datos en esa etapa. El árbol continúa este proceso hasta alcanzar un nodo hoja, donde se emite la predicción o decisión final.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6cdff3e715","additionalClasses":"headline-decoration","type":"heading2","lines":["Conceptos clave de los árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-32480c14e0","text":"\u003Cp\u003ELos árboles de decisión suelen componerse de cuatro elementos:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENodos raíz\u003C/h3\u003E\n\u003Cp\u003EAl igual que en un árbol físico, el nodo raíz es el punto de partida. Es la primera etapa del razonamiento, donde se considera el conjunto de datos completo relacionado con una o varias preguntas antes de realizar cualquier división. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERamas\u003C/h3\u003E\n\u003Cp\u003ELas ramas dividen el conjunto de datos en función de los valores presentes en los datos. Por ejemplo, los clientes mayores de 30 años pueden seguir una ruta distinta a la de los menores de 30. El árbol de decisión conduce a cada grupo hacia su resultado correspondiente.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENodos internos\u003C/h3\u003E\n\u003Cp\u003ELos nodos internos son puntos de decisión en los que el modelo formula una pregunta sobre los datos para determinar el siguiente paso. Por ejemplo, el modelo de una empresa de retail puede analizar el historial de compras y plantear la pregunta: “¿el cliente A suele comprar camisas rojas o azules?”\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENodos hoja\u003C/h3\u003E\n\u003Cp\u003ELos nodos hoja son los puntos finales del árbol, donde concluye el razonamiento y el modelo genera un resultado. Siguiendo con el ejemplo anterior, si el historial del cliente indica preferencia por camisas rojas, el árbol de decisión del modelo de ML puede conducir a un nodo hoja que active la recomendación de nuevas camisas rojas.\u003C/p\u003E\n\u003Cp\u003EEn la práctica, los árboles de decisión exploran múltiples rutas de forma simultánea. Su lógica se ramifica en distintas direcciones hasta identificar la mejor respuesta.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8e1428a77c","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b9c98af08f","text":"\u003Cp\u003EExisten numerosos algoritmos habituales de árboles de decisión, la mayoría aplicables tanto a tareas de clasificación como de regresión. Entre ellos se incluyen: \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECART (árboles de clasificación y regresión)\u003C/h3\u003E\r\n\u003Cp\u003ECART es un algoritmo ampliamente utilizado que se diferencia de otros métodos de árboles de decisión porque siempre genera divisiones binarias (sí/no) para cada característica, seleccionando la que mejor separa los valores en los datos. Por ejemplo, un modelo CART que evalúa la aprobación de un préstamo podría dividir primero a los solicitantes según “ingresos > 50.000 USD” (sí/no) y continuar dividiendo cada grupo en función de otros factores, por ejemplo: si la puntuación crediticia supera 750 o si el solicitante tiene empleo.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EID3 (Iterative Dichotomiser 3)\u003C/h3\u003E\r\n\u003Cp\u003EComo uno de los primeros algoritmos de árboles de decisión en popularizarse, ID3 divide los datos en grupos más pequeños seleccionando preguntas que reducen progresivamente las posibles respuestas hasta alcanzar la predicción o recomendación deseada. Por ejemplo, un filtro de correo no deseado (o spam) puede identificar correos que contienen la palabra “oferta”, frecuente en publicidad comercial.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EC4.5\u003C/h3\u003E\r\n\u003Cp\u003EC4.5 construye árboles de decisión mediante una serie de preguntas binarias (sí/no) que dividen los datos en subconjuntos más manejables, lo que facilita obtener predicciones más precisas. Mejora a ID3 al gestionar tanto valores categóricos (como “spam” o “no spam”) como numéricos (como “edad” o “ingresos”), además de manejar situaciones como datos ausentes. Por ejemplo, una empresa de telecomunicaciones puede utilizar C4.5 para ponderar variables como edad, ubicación y consumo de datos con el fin de proponer planes adecuados para un cliente, incluso con información parcial del cliente potencial. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECHAID (Chi-Square Automatic Interaction Detection)\u003C/h3\u003E\r\n\u003Cp\u003ECHAID emplea pruebas estadísticas para determinar dónde realizar las divisiones, generando a menudo ramas con varias opciones simultáneamente. Por ejemplo, una empresa de retail puede utilizarlo para agrupar clientes en tramos de edad (adolescentes, jóvenes adultos, personas de mediana edad y sénior) y predecir qué segmento demográfico responderá mejor a un nuevo programa de fidelización.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EÁrboles de inferencia condicional\u003C/h3\u003E\r\n\u003Cp\u003ELos árboles de inferencia condicional reducen el sesgo al evaluar si una variable es lo suficientemente relevante como para justificar una división. De esta forma, se diferencian de árboles de decisión habituales como ID3 y CART, que dividen los datos paso a paso sin comprobar la significación estadística de una variable. Por ejemplo, un árbol de decisión convencional podría priorizar la “universidad de procedencia”, mientras que uno de inferencia condicional podría descartarla por no resultar estadísticamente significativa para predecir el desempeño laboral.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7d2540355e","additionalClasses":"headline-decoration","type":"heading2","lines":["Criterios de división en árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d08f44984d","text":"\u003Cp\u003EAl dividir los datos, los modelos de ML suelen basarse en uno de estos dos criterios: impureza de Gini o entropía. Ambos miden el grado de heterogeneidad de los datos y el algoritmo aplica el criterio seleccionado para identificar la división más eficaz.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EImpureza de Gini\u003C/h3\u003E\n\u003Cp\u003EGini evalúa en qué medida una pregunta separa los datos en grupos bien definidos. Desde el punto de vista matemático, representa la probabilidad de clasificar incorrectamente un elemento aleatorio si se etiquetara según la distribución del grupo. El algoritmo CART utiliza esta métrica para comparar distintas divisiones y elegir la que produce la separación más clara. Por ejemplo, preguntar si una persona está cansada puede generar dos grupos: quienes probablemente tomarán café y quienes no.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEntropía\u003C/h3\u003E\n\u003Cp\u003ELa entropía mide la incertidumbre de un conjunto de datos. Algoritmos como ID3 y C4.5 la emplean para calcular la ganancia de información, es decir, la reducción de incertidumbre que aporta una división. El árbol selecciona la división que más reduce la incertidumbre, logrando así una separación más nítida entre clases. En la analogía del café, preguntar si es por la mañana o por la tarde reduce la incertidumbre al separar a las personas en grupos más definidos que orientan la decisión.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-5bdec71c05","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Para qué se utilizan los árboles de decisión?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-afabfb352e","text":"\u003Cp\u003EEn el aprendizaje automático, los árboles de decisión ayudan a los modelos a transformar datos sin procesar en información útil. Esto resulta especialmente valioso en sectores donde las decisiones deben estar sólidamente fundamentadas y ser fiables.\u003C/p\u003E\n\u003Cp\u003EAlgunos de sus usos más habituales son: \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEstrategia y planificación empresariales\u003C/h3\u003E\n\u003Cp\u003ELos modelos de ML entrenados con árboles de decisión permiten anticipar variables como el crecimiento de ventas, las tendencias de precios, la tasa de abandono de clientes y los niveles de demanda e inventario de la cadena de suministro. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEvaluación y mitigación del riesgo\u003C/h3\u003E\n\u003Cp\u003EEn finanzas y seguros, los árboles de decisión facilitan la evaluación de riesgos como impagos, siniestros u otras pérdidas. Al analizar las rutas de las ramificaciones de datos de clientes, como historiales crediticios, niveles de ingresos o patrones de reclamación, ayudan a actuarios, suscriptores y analistas financieros a estimar riesgos con mayor precisión.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESegmentación y focalización de clientes \u003C/h3\u003E\n\u003Cp\u003ELos equipos de marketing pueden utilizar modelos de árboles de decisión para segmentar a los \u003Ca href=\"https://www.snowflake.com/es/fundamentals/customer-segmentation/\"\u003Eclientes\u003C/a\u003E según su comportamiento de compra, perfil demográfico y actividad en línea. Esto permite a las empresas ofrecer propuestas más personalizadas y predecir qué clientes tienen mayor probabilidad de responder a una campaña.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDiagnóstico y tratamiento médico\u003C/h3\u003E\n\u003Cp\u003ELos modelos de ML en el ámbito sanitario suelen apoyarse en árboles de decisión para interpretar datos clínicos. Por ejemplo, pueden ponderar síntomas, resultados de pruebas y antecedentes familiares para obtener información clave que respalde diagnósticos y tratamientos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDetección de fraude financiero\u003C/h3\u003E\n\u003Cp\u003ELos bancos y otras entidades financieras con baja tolerancia al riesgo pueden emplear modelos de árboles de decisión para identificar actividades sospechosas. Al analizar patrones como importes de compra o devoluciones, los modelos pueden señalar transacciones que apunten a fraude, blanqueo de capitales u otras actividades ilícitas. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8ad3038ded","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas de los árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-75658d63b4","text":"\u003Cp\u003ELos árboles de decisión simplifican procesos de razonamiento complejos, permitiendo obtener resultados con mayor rapidez y eficiencia. Entre sus principales ventajas se encuentran: \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EInterpretabilidad sencilla\u003C/h3\u003E\r\n\u003Cp\u003ELa transparencia de los árboles de decisión aporta claridad al proceso de razonamiento del ML. Cualquier persona puede seguir visualmente la lógica paso a paso que conduce a las conclusiones y recomendaciones del modelo.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMenor esfuerzo en la preparación de datos\u003C/h3\u003E\r\n\u003Cp\u003ELos árboles de decisión gestionan tanto variables categóricas como numéricas, lo que reduce la necesidad de transformar o reformatear datos previamente por parte de los analistas. Disminuyen el trabajo de preparación previo a la ejecución del modelo. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAlta flexibilidad\u003C/h3\u003E\r\n\u003Cp\u003ECada árbol de decisión puede adaptarse a distintos problemas, ya que es un modelo autónomo capaz de generar predicciones de forma independiente. Además, su diseño permite combinar múltiples árboles y agregar sus resultados para abordar tareas más complejas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGestión de valores ausentes\u003C/h3\u003E\r\n\u003Cp\u003EA diferencia de otros modelos que requieren conjuntos de datos completos, los árboles de decisión pueden operar con información incompleta. Para ello, asignan menor peso a registros incompletos o dividen los datos en distintas rutas posibles.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EBuen rendimiento con conjuntos de datos pequeños\u003C/h3\u003E\r\n\u003Cp\u003ELos árboles de decisión pueden identificar patrones relevantes sin necesidad de grandes volúmenes de datos. Son eficaces incluso cuando la información es limitada, lo que los hace especialmente útiles en contextos donde los datos son escasos o costosos de obtener.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"types","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-b941673fe3","additionalClasses":"headline-decoration","type":"heading2","lines":["Limitaciones de los árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-0e512d0c2d","text":"\u003Cp\u003EA pesar de sus ventajas, los árboles de decisión también presentan limitaciones. Entre los desafíos más habituales a la hora de usar árboles de decisión para el razonamiento se encuentran:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EPropensión al sobreajuste\u003C/h3\u003E\r\n\u003Cp\u003ELos árboles de decisión pueden volverse excesivamente complejos y ajustarse a particularidades de los datos de entrenamiento en lugar de capturar patrones generales. Como resultado, el modelo puede mostrar alta precisión en la fase de entrenamiento pero rendir peor ante datos nuevos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESensibilidad al ruido\u003C/h3\u003E\r\n\u003Cp\u003ELas variaciones aleatorias o irrelevantes en un conjunto de datos pueden afectar a los árboles de decisión. Incluso pequeñas cantidades de ruido pueden provocar divisiones engañosas y predicciones inestables.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EPosibles divisiones sesgadas\u003C/h3\u003E\r\n\u003Cp\u003ESi una característica domina el conjunto de datos, el árbol de decisión puede otorgarle un peso excesivo frente a otros factores igual o más relevantes. Por ejemplo, si un modelo médico atribuye más importancia al código postal que a otros factores como la dieta o el estilo de vida, puede generar predicciones, diagnósticos y recomendaciones poco precisos. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMenor precisión que los métodos conjuntos o de ensamble\u003C/h3\u003E\r\n\u003Cp\u003EUn árbol de decisión individual toma decisiones de forma aislada, lo que puede aumentar el riesgo de error o sobreajuste. Los métodos conjuntos, en cambio, combinan los resultados de múltiples árboles y, por lo general, ofrecen resultados más precisos, robustos y consistentes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy_copy":{"id":"title-v2-30619e92d7","additionalClasses":"headline-decoration","type":"heading2","lines":["Prácticas recomendadas para árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-16b9cf3cc6","text":"\u003Cp\u003EPara maximizar el rendimiento de los árboles de decisión en ML, las organizaciones pueden aplicar las siguientes recomendaciones: \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESeleccionar características relevantes\u003C/h3\u003E\r\n\u003Cp\u003EPrioriza factores que aporten mayor capacidad de separación en los datos, como el importe de las transacciones en la detección de fraude o los resultados clínicos en el diagnóstico médico. Las características con alto poder predictivo favorecen resultados más claros y evitan divisiones innecesarias.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAplicar poda para evitar el sobreajuste\u003C/h3\u003E\r\n\u003Cp\u003EAl igual que un jardinero poda ramas para favorecer el crecimiento saludable de un árbol, es fundamental eliminar divisiones superfluas en los árbol de decisión. La poda ayuda a que el modelo busque patrones que conduzcan a resultados significativos y no se limite a memorizar los datos de entrenamiento. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EValidar con datos nuevos\u003C/h3\u003E\r\n\u003Cp\u003EEvalúa el rendimiento del árbol con conjuntos de datos que no haya utilizado durante el entrenamiento. Esto contribuye a prevenir el sobreajuste.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESupervisar las divisiones\u003C/h3\u003E\r\n\u003Cp\u003EMuchas bibliotecas de ML ofrecen herramientas para identificar las características más influyentes y visualizar cómo se realizan las divisiones. Estos mecanismos ayudan a comprender cómo el modelo procesa la información, razona y llega a sus resultados.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"]},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"container_copy_2083166428":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-2eb4838970","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-732c94cfe6","text":"\u003Cp\u003ELos árboles de decisión son una opción popular en el aprendizaje automático por su simplicidad, claridad y flexibilidad. Resultan útiles en múltiples escenarios empresariales, como la evaluación del riesgo crediticio, la previsión de ventas o la segmentación de clientes en marketing. A medida que las organizaciones buscan soluciones de inteligencia artificial (IA) y ML fiables, los árboles de decisión seguirán siendo un enfoque relevante para generar predicciones y recomendaciones.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_20831":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-399a24532d","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre árboles de decisión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-e5f06537a0","showDivider":false,"accordionItemsList":[{"title":"¿Pueden las herramientas de IA crear árboles de decisión? ","richText":"\u003Cp\u003ESí. Herramientas como ChatGPT o Gemini pueden generar árboles de decisión en formato texto, diagramas o incluso código en Python para entrenar y visualizar árboles basados en \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-modeling/\"\u003Econjuntos de datos\u003C/a\u003E.\u003C/p\u003E\n"},{"title":"¿Cuál es el papel de los árboles de decisión en la IA? ","richText":"\u003Cp\u003ELos árboles de decisión desempeñan distintas funciones en el razonamiento basado en \u003Ca href=\"https://www.snowflake.com/es/fundamentals/ai-models/\"\u003EIA\u003C/a\u003E y ML. En \u003Ca href=\"https://www.snowflake.com/es/fundamentals/regression-models/\"\u003EML\u003C/a\u003E, utilizan \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-governance/\"\u003Edatos\u003C/a\u003E para predecir resultados como el riesgo crediticio o las previsiones de ventas. En \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-observability/\"\u003EIA\u003C/a\u003E, actúan como herramientas de razonamiento que estructuran opciones y orientan la toma de decisiones. La diferencia clave es que en ML los árboles aprenden a partir de los datos, mientras que en IA ayudan a los sistemas en la toma de decisiones.\u003C/p\u003E\n"},{"title":"¿Cuál es la diferencia entre un árbol de decisión y un bosque aleatorio? ","richText":"\u003Cp\u003EUn árbol de decisión es un modelo que formula una serie de preguntas basadas en datos hasta alcanzar un resultado concreto. Un bosque aleatorio, en cambio, construye múltiples árboles de decisión sobre distintos subconjuntos de datos y características, y combina sus resultados para generar una predicción final. \u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customers","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ab96250680","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usan Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-0bc9e30649","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-a6f567ad92","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-8bd2e150c8","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Merkle mejora la experiencia del cliente a la vez que garantiza la gobernanza y la seguridad de los datos"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Leer la historia"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14aa7cce-1eb2-4108-b9c1-8127913ee48c/merkle-customer-card%25402x.jpg?preferwebp=true&quality=85","alt":"merkle logo","lazyEnabled":true,"height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EMerkle, una empresa de dentsu, consolida los datos confidenciales y colabora con clientes en Snowflake. El resultado es un entorno de datos más eficiente y fiable que agiliza el acceso a los datos y reduce el riesgo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-b68f7f80f1","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-388f3dc78b","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Tapestry desarrolla su propia plataforma de analíticas de clientes con Snowflake 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