What is train-test split in machine learning?
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What is train-test split in machine learning?
Train-test split is a technique used to evaluate the performance of a machine learning model by dividing the dataset into two parts: one for training and one for testing.
Why is it important to split the data?
It's important to split the data to ensure that the model can generalize well to unseen data, preventing overfitting.
How do I perform a train-test split?
You can perform a train-test split using libraries like scikit-learn in Python, which provides a function called train_test_split.
What ratio should I use for the split?
A common ratio is 80/20 or 70/30 for training and testing, but it can vary based on the dataset size and problem.
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