Why do we split data into training and test sets? What are the common splitting ratios, and what happens if we skip this step?

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Splitting data into training and test sets is essential for evaluating how well a machine learning model will perform on new, unseen data. This practice helps us estimate the model's ability to generalize beyond the examples it learned from.