Lizeth Llanos

Predictability of seasonal precipitation across major crop growing areas in Colombia hdl.handle.net/10568/97569 @CIAT_ @MyCIAT @cgiarclimate_LA

🤝Con el apoyo del Ministerio TIC – Colombia y guiado por nuestro investigador de #AcciónClimáticaALC, Emmanuel Zapata-Caldas, puedes participar en un taller gratuito y certificado que te dará herramientas para innovar con propósito. Regístrate 👉🏽 allbiociat.org/3V3jNRg

We’ve kicked off our KoboToolbox & data management workshop with genebank teams! From form building to field simulation, we’re strengthening digital data skills for collecting missions. 🤝 Powered by @CropTrust & @BiovIntCIAT_eng #DigitalGenebanks #KoboToolbox #BOLDERproject

As part of #BOLDProject, researchers from @BiovIntCIAT_eng and partners improved data on opportunity crops in Ghana, Benin, Uganda & Tanzania: 🌱 Identified conservation gaps 📊 Enhanced genebank data 🌍 Strengthened collection planning 🎥 Watch more: buff.ly/tawsrL9

Excited to be at the launch of CGIAR’s new Climate Action Science Program! Great energy, inspiring ideas, and real commitment to using science to drive climate solutions. Let’s do this! @CGIAR #ClimateAction #CGIAR #ScienceForChange

From Jan 27th- Feb 7th, we’re hosting a training at @BiovIntCIAT_eng , equipping national genebank staff from Ghana, Tanzania, Uganda and Benin with tools to identify gaps in crop collections and improve data-driven conservation strategies #Agrobiodiversity #GenebankInnovation

🔍 Bridging the gaps in crop diversity conservation! The Climate Action Lever’s Data Science team, in collaboration with @CropTrust, is leading efforts under the #BOLDER project to enhance gap analysis methodologies for crop diversity conservation (1/2)

From Jan 20-31, we’re hosting a training at @BiovIntCIAT_eng , equipping national genebank staff from Ghana, Tanzania, Uganda and Benin with tools to identify gaps in crop collections and improve data-driven conservation strategies #Agrobiodiversity #GenebankInnovation

🌱Hoy en #Guatemala, @BiovIntCIAT_esp y @CIMMYTesp iniciamos el taller "Fundamentos en Análisis de Información y Minería de Datos para Agricultura" en el marco de la Iniciativa #AgriLACResiliente, con el apoyo de #Cengicaña, #ICC y @atagua_org @cgiarclimate_LA #AgriLACInitiative

One of our best working paper titles yet: Error 404, Farmer Not Found. 👩‍🌾🧑‍🌾📱 Diving into the challenges of delivering information effectively to smallholder farmers. cgspace.cgiar.org/items/87ca1211…

🙌🎉¡Somos parte del diplomado de @ICESI en Agricultura Digital! 👉 #tab-1577" target="_blank" rel="nofollow noopener">icesi.edu.co/educacion_cont… En el marco de las iniciativas del @CGIAR , #AgriLACInitiative y #DigitaInnovationInitiative estamos comprometidos con la #TransformaciónDigital de los #SistemasAgroalimentarios.

¿Quieres conocer las 700.000 especies que hay en los banco de germoplasma de @CGIAR? 🌱 ¡Con muchas variedades silvestres, protegemos y compartimos semillas 🌍 para la adaptación al #CambioClimático y la #SeguridadAlimentaria! 🚀 ¡Descúbrelas en este #DíaDeLaBiodiversidad! 💚

En #AgriLACInitiative, adoptamos el enfoque de Diseño Centrado en el Humano para impulsar soluciones innovadoras e inclusivas que transformen los Sistemas Alimentarios. Conoce más aquí: uxtools4ag.org y ideo.org/tools

🌱 Descubre AClímate: tu guía para una agricultura resiliente 🌍 ¡Pronósticos agroclimáticos precisos para decisiones informadas en agricultura! Conoce de qué manera AClímate fortalece la toma de decisiones en Angola. 👇🌾 bit.ly/3U5al03

📢Hoy en #InnovaciónenAcción el turno es para el especial de #AClimate10años 🌎 Hoy evocamos los orígenes de la plataforma y aquellas lecciones aprendidas durante los 10 años de este gran aporte desde Acción Climática ALC, para el fortalecimiento en la toma de decisiones. 🌱👩‍🌾

¡Presentes en el Taller de Introducción al Análisis de Información Agroclimática para el Cultivo de #CañaPanelera con @FEDEPANELACOL! En el marco del Convenio Colombia Agroalimentaria Sostenible, trabajamos para fortalecer el sector de la panela #PanelaSostenible #Agroclima🌿🌞

Revive los mejores momentos del III Encuentro Anual de #AgriLACinitiative. 68 asistentes entre equipo técnino, científicos y socios de Mexico, Honduras, Guatemala, Peru y Colombia unidos por la construcción de redes de resiliencia en nuestra región. rb.gy/zzbjql

Nuestro investigador Jeimar Tapasco participará en el 'Conversatorio efectos del Fenómeno del Niño, Agua Potable y #Agricultura', como parte de la serie #ElPlanetaPidelaPalabra, liderado por @UNGRD. 🌎 📅Hoy a las 4:00 p.m. 👇

ROC and AUC are important concepts for evaluating classification models in business (e.g. lead scoring). In 6 minutes, I'll share what took me 60 days to figure out. Let's dive in. 1. ROC Curve: The ROC curve, which stands for Receiver Operating Characteristic curve, is a graphical representation used to evaluate the performance of a binary classifier system as its discrimination threshold is varied. 2. True Positive Rate (TPR): On the y-axis, the ROC curve plots the True Positive Rate (also known as sensitivity, or recall) which measures the proportion of actual positives that are correctly identified as such. It's calculated as TPR = TP / (TP + FN), where TP is true positives and FN is false negatives. 3. False Positive Rate (FPR): On the x-axis, the curve plots the False Positive Rate, which measures the proportion of actual negatives that are incorrectly identified as positives. It's calculated as FPR = FP / (FP + TN), where FP is false positives and TN is true negatives. 4. Thresholds: The ROC curve is created by plotting TPR against FPR at various threshold settings. A threshold in a classification algorithm is a point at which the decision is made whether a given instance belongs to a certain class. 5. Area Under the Curve (AUC): The area under the ROC curve is a measure of the effectiveness of a binary classification algorithm. An AUC of 1 represents a perfect classifier, while an AUC of 0.5 represents a worthless classifier. 6. AUC Calculation: The most common method for calculating the AUC of an ROC curve is by using the trapezoidal rule. This approach involves approximating the area under the curve by summing up the areas of trapezoids formed beneath the curve. 7. Interpretation: A curve closer to the top-left corner indicates a better performance. As the area under the ROC curve increases, the model is better at distinguishing between the positive and negative classes. === Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/free-rtrack-ma… And if you'd like to speed it up, I have a live workshop next week where I'll share how to use ChatGPT for Data Science: learn.business-science.io/registration-c… If you like this post, please reshare ♻️ it so others can get value.

#HubMeeting2023 @_cenavarro y Daniel Núñez nos comparten los principales retos de los servicios agroclimáticos y agricultura digital del #WP2 en Guatemala, además de los próximos pasos para fortalecer la toma de decisiones en el país. 💻🇬🇹🌱 #AgriLACInitiative