In today’s class, we delved into the fascinating world of cross-validation and test error analysis applied to polynomial models using a dataset containing 354 data points related to diabetes.
There are 354 Data sets for which we have records of all 3 variables (obesity, inactivity, diabetes) and I learnt about the K- flod cross validation.
K-fold cross-validation is a widely used technique in machine learning for assessing the performance and generalization ability of a model, especially when you have a limited amount of data. The dataset is divided into K roughly equal-sized subsets or “folds. The standard choice for K is 5 or 10, but you can choose other values based on the size of your dataset and computational resources. and I also worked on my project and we are working on the given Datasets.