Understand the significance of random data splitting in model training. Discover how splitting data subsets improves AI model accuracy and performance for real-world predictions.
Table of Contents
Question
When training a model, why should you randomly split the rows into separate subsets?
A. to train the model twice to attain better accuracy
B. to train multiple models simultaneously to attain better performance
C. to test the model by using data that was not used to train the model
Answer
C. to test the model by using data that was not used to train the model
Explanation
Randomly splitting rows into separate subsets during model training serves the purpose of option C: to test the model using data that wasn’t part of its training set. This process, commonly known as train-test splitting, helps evaluate how well the model generalizes to new, unseen data. By using a portion of the data for training and keeping another portion aside for testing, it ensures the model isn’t simply memorizing the training data but is learning underlying patterns that apply beyond that specific dataset. This validation with unseen data helps gauge the model’s performance, assessing its ability to make accurate predictions on new information, which is crucial for real-world applicability.
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