Here we use ProSpect to generate the EBL with a model for the SFH and ZH.

Este estudio evalúa la capacidad predictiva y la robustez estadística de dos modelos de aprendizaje supervisado aplicados a la clasificación de hongos. A través de un análisis riguroso de la matriz de separabilidad y la evaluación de los supuestos de independencia condicional, el reporte documenta por qué la estructura de correlaciones en los datos morfológicos limita el desempeño de Naive Bayes y cómo los modelos basados en vecindad local (KNN) logran una precisión superior al capturar interacciones complejas entre variables.

This project investigates the effects of NGN2 viral dose and NT3 treatment on neuronal differentiation efficiency in vitro. Differentiation outcomes were quantified as the proportion of MAP2-positive cells per well, derived from approximately 10,000 cells per condition. To formally assess treatment effects while accounting for the proportional nature of the data and multiple experimental conditions, a binomial generalized linear model (logistic regression) was applied. The analysis identified a clear dose-dependent effect of NGN2, with 5 MOI producing the strongest and most consistent differentiation outcome, and a smaller but significant positive contribution from NT3 treatment. Outlier detection and removal confirmed the robustness of these findings and supported an optimized differentiation protocol.

A logistic regression analysis examining associations between cardiovascular disease and modifiable health and lifestyle factors (physical activity, smoking history, body weight) using 2024 BRFSS survey data. The project emphasizes model comparison, diagnostics, and interpretation through predicted probabilities and visualizations.

This presentation describes a Shiny application that predicts the next word in a phrase using an N-gram language model.