In a supervised learning context, what is cross-validation used for?

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To increase the size of the training dataset
To evaluate the model's performance on unseen data
To reduce the dimensionality of the dataset
To cluster the data points

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Q: In a supervised learning context, what is cross-validation used for?

Solution: Cross-validation is used to evaluate the model's performance on unseen data by partitioning the dataset into training and validation sets.

Steps: 7

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 the model will perform on new, unseen data.
Step 3: Learn that cross-validation is a technique to assess the model's performance.
Step 4: Know that cross-validation works by splitting the dataset into two parts: a training set and a validation set.
Step 5: Train the model on the training set, which is the part of the data used to teach the model.
Step 6: Test the model on the validation set, which is the part of the data that the model has not seen before.
Step 7: Repeat the process multiple times with different splits of the data to get a reliable estimate of the model's performance.

In a supervised learning context, what is cross-validation used for?

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