This assignment covers Questions 13, 14, and 16 from Chapter 4 of An Introduction to Statistical Learning with Applications in R. The exercises focus on classification methods, examining how statistical models can be used to predict categorical outcomes based on a set of predictor variables. The analysis applies classification techniques in R, including logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbors (KNN). Model performance is evaluated using measures such as classification accuracy, error rates, and confusion matrices.

SVM kernel lineal polinomial y radial comparado con arboles, bosques aleatorios y regresión logística

Reporte práctico sobre la evaluación de altura de plantas de pimiento

STA 6543 An Introduction to Statistical Learning with Applications in R Second Edition Chapter 4

Just taking a look at the audit logs from Bishop PD Flock use.

Organizations BPD shares flock data with. Size is scaled by frequency of network use (for large users). Color is based on which types of camera data is shared (NA means there is no record of this, but there is record of the organization querying BPD data)