Today I invested a considerable amount of time in grasping the concept of cross validation. To summarize my understanding, cross-validation is a test for your model to see how well it can make predictions on data it hasn’t seen before. It helps us avoid a common problem called overfitting which occurs when a model learns the training data too well and becomes too specialized, performing poorly on new data. Cross-validation checks if your model is likely to overfit.
Further I decided to perform this on my Urban- Rural dataset for diagnosed diabetes percentage. I decided to use K fold cross validation to split the data into 10 sets.
I faced issues during my cross-validation process, and to address these problems, I intend to handle the missing data. My next step is to resolve these errors and proceed with the cross-validation.