I have learned critical lessons about assessing prediction error and utilizing cross-validation procedures through my investigation of CDC data comprising variables like obesity, inactivity, and diabetes. The old method known as the Validation Set Approach first appeared to be promising but had some drawbacks. Due to the arbitrary separation of the data, it frequently produced inconsistent results. My journey, however, took a fascinating turn when I learned about K-fold cross-validation. The dataset will be divided into K sections using this procedure (often 5 or 10). Repeating this process K times, each portion alternates between acting as the training set and the validation set. K-fold cross-validation offers the following benefits:
Stability: It makes error estimates less variable, giving a more reliable indicator of model performance. Efficiency: By making the most of every available data piece, the evaluation is improved. Model selection helps determine the ideal level of model complexity by analysing performance over multiple iterations. Accurate Test Error Estimation: This technique provides reliable information on how well a model performs in practise. But there are drawbacks. Predictor selection should not be neglected, and performing cross-validation after the second stage can result in severe bias and artificially low error rates. Apply cross-validation to the processes of model fitting and predictor selection to prevent this by making sure the entire process is thoroughly evaluated. In conclusion, my experience with the CDC data taught me the value of thorough model assessment. To fully utilise the potential of our data for more trustworthy models, estimating prediction error, embracing cross-validation, and avoiding common mistakes are essential