Understand the significance of splitting data into training and validation sets in machine learning. Explore the benefits of unbiased model assessment and prevention of overfitting for accurate predictions!
Why do you split data into training and validation sets?
Splitting data into two sets enables you to compare the labels that the model predicts with the actual known labels in the original dataset.
Splitting data into training and validation sets serves to assess machine learning models effectively. This segregation enables model training on one portion of the dataset (training set) while reserving another portion (validation set) for model evaluation. It helps gauge how well the model generalizes to new, unseen data, thereby minimizing overfitting and providing an unbiased estimate of model performance.
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