Today’s discussion was about the topics –
K-fold cross-validation is a popular machine learning technique for evaluating the performance of a predictive model and decreasing the danger of overfitting. It is especially useful when you have a limited amount of data and want to make the most of it while still generating a credible estimate of the performance of your model. The following is how K-fold cross-validation works:
Dataset Splitting: Begin with your dataset and divide it into K subgroups of nearly similar size. These subsets are frequently referred to as “folds.”
Training and Testing: You train and test your model K times, using a different fold as the test set and the remaining K-1 folds as the training set each time. If you use 5-fold cross-validation, for example, you will repeat the process five times, each time using a different fifth of the data as the test set.
Performance Metrics: After each run, you compute a performance metric (such as accuracy, F1 score, or mean squared error) to assess how well your model did on the test set.
Average performance: measures from all K runs to receive an overall evaluation of your model’s performance. When compared to a single train-test split, this average provides a more trustworthy prediction of how well your model is expected to perform on unknown data.