What is the purpose of using 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 the dimensionality of the dataset
To improve the interpretability of the model

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

Step 1: Understand that cross-validation is a technique used in machine learning.
Step 2: Know that the goal of cross-validation is to check how well a model performs on new, unseen data.
Step 3: Realize that to do this, we split our data into different parts or subsets.
Step 4: Use some of these subsets to train the model (this is called the training set).
Step 5: Use the remaining subsets to test the model (this is called the validation set).
Step 6: Repeat this process several times, each time using different subsets for training and validation.
Step 7: Finally, analyze the results to see how well the model can predict new data.

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