Cross validation provides a robust evaluation of a model’s effectiveness, which is crucial for businesses relying on data-driven decision making. Key reasons for its importance include:
1. Model Selection: It assists in comparing different models to identify the one that performs best on unseen data. 2. Avoiding Overfitting: It helps in ensuring that the model is not too tailored to the specific training data, which would make it less effective on new data. 3. Resource Allocation: It helps businesses allocate resources more efficiently by predicting outcomes more accurately.