K- cross Validation
K-fold cross-validation is a technique commonly used in machine learning and statistical modeling to assess the performance and generalization ability of a predictive model. It is particularly helpful when you have a limited amount of data and want to make efficient use of it while avoiding overfitting. K-fold cross-validation involves the following steps:
- Data Splitting
- Model Training and Evaluation
- Performance Metric Calculation
- Cross-Validation Results