What are the best practices for splitting your dataset in predictive modeling?
In predictive modeling, how you split your dataset can significantly impact the performance of your models. Proper data splitting ensures that you have a balanced representation of data for training, validation, and testing. This process is critical for evaluating the model's ability to generalize to new, unseen data and for preventing issues like overfitting, where the model performs well on the training data but poorly on new data. By following best practices in dataset splitting, you can build more reliable and robust predictive models.
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Set a random seed:Consistency in data splitting is key. By setting a random seed, you ensure that you get the same train-test split every time, which is crucial for comparing model performance reliably.
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Stratified sampling:When dealing with imbalanced classes, using stratified sampling maintains the proportion of classes across your data subsets, leading to more accurate and fair predictive models.