What are the steps for effectively applying cross-validation to a machine learning model?

Cross-validation is a key technique for properly testing a machine learning model before it is implemented. It makes sure the model is truly effective and to measure its generalizability. Here are the steps for effectively applying cross-validation to a machine learning model:

• Set aside a portion of data for testing purposes
• Segment the data for training
• Create the model and set appropriate hyperparameters
• Train the model on the training data set
• Evaluate model performance on the testing set
• Adjust hyperparameters as needed to improve performance
• Re-train the model with the adjusted hyperparameters
• Evaluate again using testing set
• Ensure the model is sufficiently generalizable

When applying cross-validation to a machine learning model, it helps to follow these steps in order to produce an effective and generalizable model for your application.

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