class: title-slide, center, middle <img class="logo" src="https://www.univalle.edu.co/images/Univalle/70_Rojo.jpg" alt="Universidad del Valle"> # Clasificación de la Esperanza de Vida a partir de Factores Socioeconómicos y Ambientales ### Aplicación de modelos de aprendizaje supervisado: Logit y KNN .autores[ **Miguel Fabricio Arguello Cedeño · Laura Valentina Aguirre Reyes** **Jaider Fernando Cuaspud Telpiz · Juan José Ibarguen Cárdenas** ] .inst[Universidad del Valle · Viernes 23 de octubre de 2026] ??? Tiempo: ~20 s. Presentar el título, al equipo y la fecha. Anunciar el recorrido: los datos, los dos modelos (Logit y KNN), su comparación y la recomendación final. --- # Pregunta de investigación y objetivo .centrar[ .caja.destacado[ **¿Es posible clasificar si un país, en un año dado, tiene una esperanza de vida Alta (70 años o más)** a partir de su desnutrición, gasto en salud, saneamiento, desempleo y emisiones de CO2? ] **Objetivo:** construir y comparar dos modelos de aprendizaje supervisado para clasificar la esperanza de vida en **Alta** o **Baja**, y recomendar el más adecuado. .fila[ .tarj[ ### Regresión logística (Logit) Modelo **paramétrico** e **interpretable**: entrega un odds ratio para cada variable. ] .tarj[ ### K vecinos más cercanos (KNN) Modelo **no paramétrico**: clasifica según la **cercanía** a observaciones de entrenamiento. ] ] .cajapeq[ Alta: 70 años o más · Baja: menos de 70 años. El umbral de 70 años es un criterio de referencia que simplifica una variable continua. ] ] ??? Tiempo: ~40 s. Leer la pregunta de investigación y el objetivo. Contrastar las dos aproximaciones: una paramétrica y fácil de interpretar (Logit) frente a una no paramétrica basada en la proximidad entre observaciones (KNN). Aclarar que Alta y Baja se definen con el umbral de 70 años, que es un criterio de referencia definido por el grupo. --- # Datos y diseño del estudio .centrar[ ### Datos del Banco Mundial <div class="kpi-fila"><div class="kpi"><div class="kpi-v">1.729</div><div class="kpi-l">Observaciones país-año</div></div><div class="kpi"><div class="kpi-v">92</div><div class="kpi-l">Países</div></div><div class="kpi"><div class="kpi-v">2001–2019</div><div class="kpi-l">Periodo</div></div><div class="kpi"><div class="kpi-v">74,6 %</div><div class="kpi-l">Con esperanza de vida Alta</div></div></div> ### Partición temporal y validación <div class="kpi-fila"><div class="kpi dis"><div class="kpi-v">≤ 2015</div><div class="kpi-l">Entrenamiento · 1.367 obs.</div></div><div class="kpi dis"><div class="kpi-v">≥ 2016</div><div class="kpi-l">Prueba · 362 obs.</div></div><div class="kpi dis"><div class="kpi-v">Estandarizar</div><div class="kpi-l">solo con media y DE del entrenamiento</div></div><div class="kpi dis"><div class="kpi-v">Países nuevos</div><div class="kpi-l">evaluación adicional por países</div></div></div> .cajapeq[ La categoría **Alta** representa el **74,6 %** de las observaciones. Se descartó el 44,5 % de los registros por datos faltantes; los descartados tienen 39,8 % de esperanza Alta, de modo que los resultados corresponden a los países con información completa. ] ] ??? Tiempo: ~50 s. La base de modelado tiene las observaciones, los países y el periodo de las tarjetas, y la categoría Alta representa la mayor parte de las observaciones. El clasificador se construye con el pasado (años hasta 2015) y se evalúa con 2016 en adelante, que reproduce el uso real. La estandarización usa solo la media y la desviación estándar del entrenamiento para no filtrar información. Además de esta partición temporal se hace una validación con países nuevos, que veremos al final. --- # Variables y separación de las clases .fila[ .estrecha[ <div class="datatables html-widget html-fill-item" id="htmlwidget-bb1c78f0c5982999d72b" style="width:100%;height:auto;"></div> <script type="application/json" data-for="htmlwidget-bb1c78f0c5982999d72b">{"x":{"filter":"none","vertical":false,"data":[["<b>Desnutrición<\/b><br><span class='sub'>Prevalencia de desnutrición<\/span>","<b>Salud<\/b><br><span class='sub'>Gasto en salud (% del PIB)<\/span>","<b>Saneamiento<\/b><br><span class='sub'>Acceso a saneamiento básico<\/span>","<b>Desempleo<\/b><br><span class='sub'>Tasa de desempleo<\/span>","<b>logCO2<\/b><br><span class='sub'>Logaritmo de las emisiones de CO2<\/span>"],["Negativo ✓","Positivo ✓","Positivo ✓","Incierto","Incierto"]],"container":"<table class=\"compact stripe hover row-border\">\n <thead>\n <tr>\n <th>Variable<\/th>\n <th>Signo esperado<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"t","paging":false,"searching":false,"autoWidth":false,"columnDefs":[{"className":"dt-center","targets":"_all"},{"name":"Variable","targets":0},{"name":"Signo esperado","targets":1}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script> ] .ancha[ <div id="htmlwidget-df078f265fe6405d39d8" style="width:900px;height:290px;" class="plotly html-widget"></div> <script type="application/json" 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En **3 de 3** variables con signo esperado definido, la diferencia observada coincide con la hipótesis (✓). Son asociaciones, no causalidad. ] ??? Tiempo: ~55 s. Tabla: qué mide cada variable y el signo esperado; la marca indica si la diferencia observada entre clases coincide con esa hipótesis. Gráfico: en las dos primeras variables las cajas de Baja y Alta casi no se solapan, mientras que en la última se parecen mucho. logCO2 tenía signo incierto: refleja industrialización e ingreso más que un efecto sobre la salud. 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La línea punteada separa entrenamiento y prueba. De los 92 países, 63 son siempre Alta, 17 siempre Baja y solo 12 cruzan el umbral. ] ??? Tiempo: ~40 s. Hay un desbalance moderado: la clase Alta predomina. Un clasificador que siempre diga Alta ya acierta ese porcentaje, así que la exactitud sola no basta para comparar los modelos y hay que mirar también sensibilidad, especificidad y AUC. La proporción de Alta crece en el tiempo porque unos pocos países cruzan el umbral de 70 años; la prueba no incluye países nuevos. --- # Logit: 4 de 5 variables significativas .fila[ .ancha[ <div id="htmlwidget-b11088a2e4d4beb88f33" style="width:960px;height:300px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-b11088a2e4d4beb88f33">{"x":{"data":[{"x":[0,0],"y":[0.40000000000000002,5.5999999999999996],"text":"","type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(127,127,127,1)","dash":"dash"},"hoveron":"points","showlegend":false,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[-0.83262615651906779,-0.54902602965496916,null,0.38702420054352077,0.63734689905927799,null,0.4249832894041749,0.74885896065223345,null,-0.030881551712728529,0.15726373820157788,null,0.31514081568744851,0.51818667598215928],"y":[1,1,null,4,4,null,5,5,null,2,2,null,3,3],"text":["Desnutrición<br>OR por 1 DE = 0,20<br>IC 95 %: 0,15 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R² de McFadden = **0,621**. VIF máximo = **1,38** (sin colinealidad problemática). ] ??? Tiempo: ~60 s. El gráfico muestra el odds ratio por desviación estándar de cada variable, de modo que las cinco quedan en la misma escala; los intervalos que cruzan la línea punteada en 1 corresponden a variables sin asociación significativa. Las significativas son las de la tarjeta superior y la que no lo fue está en la tarjeta naranja. El R² de McFadden indica el ajuste del modelo y el VIF descarta colinealidad problemática. El signo positivo de logCO2 no indica un efecto benéfico de las emisiones: refleja el nivel de industrialización de la economía. --- # KNN: la validación cruzada elige k = 3 .fila[ .ancha[ <div id="htmlwidget-d96222b5190c23aef698" style="width:960px;height:290px;" class="plotly html-widget"></div> <script type="application/json" 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= 3<br>Exactitud = 0,9878","K = 5<br>Exactitud = 0,9846","K = 7<br>Exactitud = 0,9768","K = 9<br>Exactitud = 0,9656","K = 11<br>Exactitud = 0,9602","K = 13<br>Exactitud = 0,9532","K = 15<br>Exactitud = 0,9490","K = 17<br>Exactitud = 0,9466","K = 19<br>Exactitud = 0,9449","K = 21<br>Exactitud = 0,9434","K = 23<br>Exactitud = 0,9388","K = 25<br>Exactitud = 0,9310","K = 27<br>Exactitud = 0,9208","K = 29<br>Exactitud = 0,9205","K = 31<br>Exactitud = 0,9212"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(59,130,246,1)","opacity":1,"size":9.8267716535433092,"symbol":"circle","line":{"width":1.8897637795275593,"color":"rgba(59,130,246,1)"}},"hoveron":"points","showlegend":false,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[3],"y":[0.98780226770351287],"text":"K = 3<br>Exactitud = 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La exactitud de validación es **optimista**: los pliegues se arman al azar y un mismo país aparece en entrenamiento y validación. ] ??? Tiempo: ~40 s. A diferencia del Logit, el KNN no entrega coeficientes: solo clasifica según la cercanía a los casos de entrenamiento. El valor de k se elige con validación cruzada y el punto oscuro marca el elegido. La exactitud de validación cruzada es optimista frente a la de la muestra de prueba, porque un mismo país aparece en entrenamiento y validación con valores muy parecidos entre años. --- # Matrices de confusión: errores opuestos .fila[ .ancha[ <div id="htmlwidget-1bf29ccc2b35d4981c93" style="width:960px;height:290px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-1bf29ccc2b35d4981c93">{"x":{"data":[{"x":[1,2],"y":[1,2],"z":[[0.16,0],[0.040000000000000001,1]],"text":[["Logit<br>Clase real: Baja<br>Clase predicha: Baja<br>Casos: 52","Logit<br>Clase real: Alta<br>Clase predicha: Baja<br>Casos: 8"],["Logit<br>Clase real: Baja<br>Clase predicha: Alta<br>Casos: 19","Logit<br>Clase real: Alta<br>Clase predicha: Alta<br>Casos: 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Asigna Alta a países que siguen por debajo de 70 años: India (4), Mongolia (4), Myanmar (4), Nigeria (3). ] .tarj.naranja[ ### KNN · error conservador **11 falsos negativos** y 0 falsos positivos. Mantiene como Baja a países que cruzaron los 70 años: Iraq (4), Nepal (3), Sao Tome and Principe (2), Suriname (2). ] ] ] .analisis[ En promedio, el **76 %** de los 3 vecinos más cercanos de cada observación de prueba pertenece al **mismo país** (94 % en los falsos negativos del KNN): el KNN funciona como una memoria de la clase histórica de cada país y falla cuando el país cambia de grupo. ] ??? Tiempo: ~60 s. La diagonal de cada matriz son los aciertos. El Logit comete falsos positivos: clasifica como Alta a países que siguen por debajo de 70 años, un error optimista y el más costoso para priorizar políticas públicas. El KNN comete falsos negativos: mantiene como Baja a países que cruzaron el umbral hace poco, un error conservador. El mecanismo es que sus vecinos más cercanos suelen ser el mismo país en años anteriores. La referencia a superar es la exactitud del clasificador trivial, 80,4 % en la prueba. --- # Comparación en la muestra de prueba .fila[ .estrecha[ <div class="datatables html-widget html-fill-item" id="htmlwidget-5c0ccca2b67304374cd6" style="width:100%;height:auto;"></div> <script type="application/json" data-for="htmlwidget-5c0ccca2b67304374cd6">{"x":{"filter":"none","vertical":false,"data":[["Exactitud","Sensibilidad","Especificidad","AUC"],["0,925","0,973","0,732","0,961"],["0,970","0,962","1,000","0,983"],["KNN","Logit","KNN","KNN"]],"container":"<table class=\"compact stripe hover row-border\">\n <thead>\n <tr>\n <th>Métrica<\/th>\n <th>Logit<\/th>\n <th>KNN (k=3)<\/th>\n <th>Mejor modelo<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"t","paging":false,"searching":false,"autoWidth":false,"columnDefs":[{"className":"dt-center","targets":"_all"},{"name":"Métrica","targets":0},{"name":"Logit","targets":1},{"name":"KNN (k=3)","targets":2},{"name":"Mejor 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(y) ","x"],"fa9048824a89":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> ] ] .analisis[ Con el corte de 0,5, el KNN tiene una especificidad de **100,0 %** frente a **73,2 %** del Logit, pues no comete falsos positivos; en sensibilidad 97,3 % y 96,2 %. Ambos modelos discriminan de forma sobresaliente (AUC de 0,9 o más). Esa ventaja se evalúa más adelante con países nuevos. ] ??? Tiempo: ~55 s. La tabla reúne las cuatro métricas principales y señala cuál modelo es mejor en cada una; las demás métricas quedan en el informe escrito. La curva ROC evalúa el orden en que cada modelo coloca los países-año sin fijar un umbral, y el AUC resume esa capacidad de separar las clases. La diferencia más grande está en la especificidad, que depende de la clase Baja, y se explica porque el KNN no comete falsos positivos en la partición temporal. Dejar planteada la pregunta: ¿esa ventaja se mantiene con países que el modelo no vio? --- # Efecto del umbral: índice de Youden <div id="htmlwidget-dfdaeb2f0e40b5eb0955" style="width:1500px;height:270px;" class="plotly html-widget"></div> <script type="application/json" 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0.05<br />Valor: 0.2957746<br />Metrica: Especificidad","Umbral: 0.06<br />Valor: 0.3521127<br />Metrica: Especificidad","Umbral: 0.07<br />Valor: 0.3802817<br />Metrica: Especificidad","Umbral: 0.08<br />Valor: 0.4225352<br />Metrica: Especificidad","Umbral: 0.09<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.10<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.11<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.12<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.13<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.14<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.15<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.16<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.17<br />Valor: 0.4507042<br />Metrica: Especificidad","Umbral: 0.18<br />Valor: 0.4647887<br />Metrica: Especificidad","Umbral: 0.19<br />Valor: 0.4647887<br />Metrica: Especificidad","Umbral: 0.20<br />Valor: 0.4788732<br />Metrica: Especificidad","Umbral: 0.21<br />Valor: 0.4929577<br />Metrica: Especificidad","Umbral: 0.22<br />Valor: 0.4929577<br />Metrica: Especificidad","Umbral: 0.23<br />Valor: 0.5070423<br />Metrica: Especificidad","Umbral: 0.24<br />Valor: 0.5070423<br />Metrica: Especificidad","Umbral: 0.25<br />Valor: 0.5070423<br />Metrica: Especificidad","Umbral: 0.26<br />Valor: 0.5070423<br />Metrica: Especificidad","Umbral: 0.27<br />Valor: 0.5211268<br />Metrica: Especificidad","Umbral: 0.28<br />Valor: 0.5352113<br />Metrica: Especificidad","Umbral: 0.29<br />Valor: 0.5352113<br />Metrica: Especificidad","Umbral: 0.30<br />Valor: 0.5352113<br />Metrica: Especificidad","Umbral: 0.31<br />Valor: 0.5352113<br />Metrica: Especificidad","Umbral: 0.32<br />Valor: 0.5492958<br />Metrica: Especificidad","Umbral: 0.33<br />Valor: 0.5633803<br />Metrica: Especificidad","Umbral: 0.34<br />Valor: 0.5915493<br />Metrica: Especificidad","Umbral: 0.35<br />Valor: 0.6056338<br />Metrica: 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0.51<br />Valor: 0.7323944<br />Metrica: Especificidad","Umbral: 0.52<br />Valor: 0.7464789<br />Metrica: Especificidad","Umbral: 0.53<br />Valor: 0.7464789<br />Metrica: Especificidad","Umbral: 0.54<br />Valor: 0.7464789<br />Metrica: Especificidad","Umbral: 0.55<br />Valor: 0.7464789<br />Metrica: Especificidad","Umbral: 0.56<br />Valor: 0.7464789<br />Metrica: Especificidad","Umbral: 0.57<br />Valor: 0.7605634<br />Metrica: Especificidad","Umbral: 0.58<br />Valor: 0.7746479<br />Metrica: Especificidad","Umbral: 0.59<br />Valor: 0.7746479<br />Metrica: Especificidad","Umbral: 0.60<br />Valor: 0.7887324<br />Metrica: Especificidad","Umbral: 0.61<br />Valor: 0.8028169<br />Metrica: Especificidad","Umbral: 0.62<br />Valor: 0.8028169<br />Metrica: Especificidad","Umbral: 0.63<br />Valor: 0.8028169<br />Metrica: Especificidad","Umbral: 0.64<br />Valor: 0.8028169<br />Metrica: Especificidad","Umbral: 0.65<br />Valor: 0.8028169<br />Metrica: Especificidad","Umbral: 0.66<br />Valor: 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Tiempo: ~40 s. La gráfica muestra cómo cambian la sensibilidad y la especificidad cuando se modifica el umbral de probabilidad para clasificar como Alta; las líneas punteadas marcan el umbral de Youden de cada modelo. Subir el umbral aumenta la especificidad a costa de la sensibilidad. Con k = 3, las probabilidades del KNN toman solo 4 valores distintos, por eso su curva avanza en escalones. 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La ventaja del KNN en la partición temporal venía de reconocer países ya vistos. ] ] ] ??? Tiempo: ~40 s. Es el análisis clave. En la partición temporal los mismos países aparecen en entrenamiento y prueba, lo que puede favorecer al KNN. Aquí cada país queda completo en un solo pliegue, así que cada modelo se evalúa con países que no vio durante el ajuste. Comparar, en la gráfica, las barras de la partición temporal con las de países nuevos: es la evidencia más firme para la recomendación. --- # Conclusión: se recomienda el Logit .centrar[ .caja.destacado[ **Se recomienda el Logit** por su mejor desempeño con países nuevos y porque permite interpretar qué variables se asocian con la longevidad. ] .fila[ .tarj[ ### Desempeño con países nuevos El Logit alcanza un AUC de 0,942 frente a 0,910 del KNN, con una especificidad de 76,1 % frente a 73,1 %. ] .tarj[ ### Interpretación de las variables Sus odds ratios muestran qué variables se asocian con una esperanza de vida alta (mayor efecto: Desnutrición y Saneamiento); el KNN no entrega coeficientes. ] ] .cajapeq.alerta[ **Principales limitaciones:** solo 92 países con datos completos · la clase Baja de la prueba proviene de 19 países · las observaciones de un mismo país no son independientes · son asociaciones, no causalidad. ] ] ??? Tiempo: ~60 s. Dar la recomendación y sus dos razones: el desempeño con países nuevos y la interpretabilidad, que era uno de los objetivos específicos. Reconocer el uso que sí tiene el KNN: clasificar, en años posteriores, países ya observados, aunque exige conservar los datos de entrenamiento y es sensible a la escala y a la elección de k. Cerrar con las limitaciones: pocos países en la clase Baja de la prueba, observaciones no independientes y ausencia de causalidad. Mensaje central: la métrica y el esquema de validación importan tanto como el algoritmo, porque una exactitud alta puede ocultar un error de un solo tipo. --- class: inverse, center, middle <img class="logo" src="https://www.univalle.edu.co/images/Univalle/70_Rojo.jpg" alt="Universidad del Valle"> # ¡Gracias! ### Preguntas y comentarios <br> .chico[Banco Mundial. *World Development Indicators* · James et al. (2021) · Hosmer et al. (2013) · Cover y Hart (1967) · McFadden (1979)] ??? Tiempo: ~15 s. Agradecer y abrir la ronda de preguntas. Total estimado de la presentación: 10 minutos.