K-Fold Cross-Validation is a helpful way to make sure machine learning models work well without making mistakes. It helps prevent something called overfitting. This happens when a model learns too much from the training data and doesn't do well on new data.
Training and Testing: You split your data into smaller groups, known as folds. The model learns using of these groups and then tests on the last group. You do this times, so each group gets a turn to be the testing group.
Average Results: After testing, you find the average accuracy from all the tests. This gives a better idea of how well your model will perform.
By using K-Fold, you can trust that your model can handle new data better. It helps lower the chance of overfitting!
K-Fold Cross-Validation is a helpful way to make sure machine learning models work well without making mistakes. It helps prevent something called overfitting. This happens when a model learns too much from the training data and doesn't do well on new data.
Training and Testing: You split your data into smaller groups, known as folds. The model learns using of these groups and then tests on the last group. You do this times, so each group gets a turn to be the testing group.
Average Results: After testing, you find the average accuracy from all the tests. This gives a better idea of how well your model will perform.
By using K-Fold, you can trust that your model can handle new data better. It helps lower the chance of overfitting!