What is the purpose of cross-validation in model selection?

To increase the size of the training dataset
To assess how the results of a statistical analysis will generalize to an independent dataset
To reduce overfitting by simplifying the model
To improve the accuracy of the model

What is the purpose of cross-validation in model selection?

Step 1: Understand that when we create a model, we want it to work well not just on the data we used to create it, but also on new, unseen data.
Step 2: Realize that cross-validation is a technique used to test how well our model performs on different sets of data.
Step 3: Learn that in cross-validation, we split our data into several parts. We train the model on some parts and test it on the remaining parts.
Step 4: Repeat this process multiple times, each time using different parts for training and testing.
Step 5: Finally, we average the results from all the tests to get a better idea of how well our model will perform on new data.

No concepts available.