Validation sets are super important when we want to make our models better. Here’s how they help:
Check Generalization: Validation sets show how well your model works with new data it hasn’t seen before. This is really important because we don’t want our model to just remember the training data.
Fine-Tune Hyperparameters: They let you try out different hyperparameters and pick the ones that do the best on the validation set. This is better than just choosing based on the training set.
Prevent Overfitting: By checking how the model does on the validation set often, you can find out if it starts to overfit. This means it’s getting too specific to the training data, and you can make changes to fix it.
In short, validation sets help make sure your model is not just perfect for the training data but is also ready for real-world challenges!
Validation sets are super important when we want to make our models better. Here’s how they help:
Check Generalization: Validation sets show how well your model works with new data it hasn’t seen before. This is really important because we don’t want our model to just remember the training data.
Fine-Tune Hyperparameters: They let you try out different hyperparameters and pick the ones that do the best on the validation set. This is better than just choosing based on the training set.
Prevent Overfitting: By checking how the model does on the validation set often, you can find out if it starts to overfit. This means it’s getting too specific to the training data, and you can make changes to fix it.
In short, validation sets help make sure your model is not just perfect for the training data but is also ready for real-world challenges!