Sampling techniques in machine learning help evaluate model performance by partitioning data in different ways. **Leave-One-Out Cross-Validation (LOOCV)** trains on all but one observation, repeating for each, while **k-Fold Cross-Validation** divides data into k subsets, training on k-1 and testing on the remaining fold. The **Validation Approach (Train-Test Split)** randomly splits data into training and testing sets, offering simplicity but higher variance. **Bootstrap Resampling** draws multiple samples with replacement to estimate model uncertainty, useful for small datasets but prone to overfitting.

Actividad 1.

Lab Spring 2025

Homework Assignment 2 for the course ADA in R