K-fold Cross-Validation Method
K-fold cross-validation is a popular approach in machine learning and statistics to evaluate the effectiveness and adaptability of a forecast model.
It becomes especially useful when dealing with sparse data to maximize its utility and prevent over-optimization. The K-fold cross-validation process encompasses:
mathematica :
Segmenting the Data
Training the Model and Assessing It
Computing Evaluation Metrics
Analyzing Cross-Validation Outcomes