What is the purpose of cross-validation in supervised learning?

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 model's accuracy on the training set

What is the purpose of cross-validation in supervised learning?

Step 1: Understand that supervised learning involves training a model on a dataset with known outcomes.
Step 2: Realize that we want to know how well our model will perform on new, unseen data.
Step 3: Learn that cross-validation helps us test the model's performance by splitting the data into different parts.
Step 4: Know that in cross-validation, we train the model on some parts of the data and test it on other parts.
Step 5: Understand that this process is repeated multiple times with different splits of the data.
Step 6: Conclude that by averaging the results from these tests, we can better estimate how well the model will work in real-world situations.

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