Today I have gained knowledge on Resampling Methods: Cross-Validation and Bootstrap by watching the videos. These Methods are statistical techniques used in data analysis and machine learning to estimate the performance of a model or to create new datasets by manipulating the existing data. They are particularly useful when we are dealing with most relevant datasets or when we want to assess the strength of a model. For example they provide estimates of t-test prediction error and the standard deviation and bias of our parameter estimates.

Cross-validation serves as a valuable technique for evaluating the ability of a predictive model to perform well on unseen data. This method involves in dividing the dataset into several partitions, commonly referred to as “K subsets” or “folds called as K fold cross validation.” The model is then trained using a subset of these folds while being tested on a distinct fold and this cycle repeats several times, with each iteration using a different fold as the test set while the rest are employed for training purposes.

Estimating Prediction error with the distinction between test error and training error. The test error predicts the response on a new observation, for which the one that was not used in the training method. Whereas the training error can be easily calculated by applying the statistical learning method to the observations used in its training method. Here we randomly divide the set of samples in to two parts: a training set and a validation set referring as a validation set-approach. This model fits on the training set and the fitted model is used to predict the responses for the observations in the validation set. This resulting validation-set error provides an estimate of the test error.

In summary, these resampling methods and insights into prediction error estimation equip me with the tools and knowledge needed to assess model performance and make data-driven decisions in the field of data analysis and machine learning.