What is 10 fold?

10-Fold Cross-Validation Explained

10-fold cross-validation is a common and widely used technique in machine learning for estimating the performance of a model on unseen data. It helps assess how well a model generalizes to new, independent datasets and is particularly useful when you have a limited amount of data.

Here's a breakdown:

• What it is: 10-fold cross-validation is a specific type of <a href="https://www.wikiwhat.page/kavramlar/cross-validation">cross validation</a>. In cross-validation, the original dataset is partitioned into multiple subsets, or "folds". The model is then trained and evaluated multiple times, using different combinations of these folds.

• The Process: In 10-fold cross-validation, the dataset is divided into 10 equally sized folds. The process is repeated 10 times. In each iteration, one fold is used as the testing set (also called the validation set), and the remaining 9 folds are used as the training set.

• Model Training and Evaluation: A machine learning model is trained on the 9 folds selected for training, and then its performance is evaluated on the single fold reserved as the testing set. A performance metric is recorded (e.g., accuracy, precision, recall, F1-score, or AUC).

• Averaging the Results: This training and evaluation process is repeated 10 times, with each fold used as the testing set exactly once. Finally, the performance metrics from each of the 10 iterations are averaged to provide an overall estimate of the model's performance. This average is a better indicator of the model's generalization ability compared to just splitting the data into a single training and testing set.

• Why 10 Folds? The number 10 is a commonly accepted standard in cross-validation. It's considered a good balance between having a reasonably large test set in each iteration (allowing for a reliable performance estimate) and repeating the process enough times to get a stable overall average. While other numbers of folds can be used (e.g., 5-fold cross-validation, leave-one-out cross-validation), 10-fold is often the preferred choice.

• Benefits:

  • Provides a more reliable estimate of model performance compared to a single train/test split.
  • Reduces the risk of overfitting, as the model is evaluated on different subsets of the data.
  • Makes more efficient use of data, especially when the dataset is small.

• Applications: 10-fold cross-validation is used in many areas of <a href="https://www.wikiwhat.page/kavramlar/machine%20learning">machine learning</a>, including:

  • Model selection: Comparing the performance of different models.
  • Hyperparameter tuning: Finding the best parameters for a particular model.
  • Estimating the generalization error of a model.

• Stratified K-Fold: A variation called stratified k-fold (with k=10) is used in classification problems. This ensures that each fold has the same proportion of examples from each class as the original dataset. This is important when dealing with imbalanced datasets.

In conclusion, 10-fold cross-validation is a robust and widely used technique for assessing the performance of machine learning models. It provides a more reliable and stable estimate of generalization error compared to simpler approaches and is a valuable tool for model selection and hyperparameter tuning.