cross-validation

Vocabulary Word

Definition

'Cross-validation' is a way we use to verify the accuracy of a statistical or predictive model. Think of it as a two-step process. First, we use part of our data set to create or 'train' our model. Then, we use the remainder of the data set to 'test' how well our model predicts outcomes. If it's really good at the test phase, we can say our model is 'validated'.

Examples in Different Contexts

In research, 'cross-validation' is used to test the robustness of a finding by applying the methodology across different data sets or experimental conditions. A researcher might discuss, 'Through cross-validation, we can confirm that our results are consistent and not dependent on specific data characteristics.'

Practice Scenarios

Technology

Scenario:

Our current prediction accuracy seems to be inconsistent across different datasets. We might need to remodel our learning algorithm.

Response:

Absolutely, I think if we apply a rigorous cross-validation, it should lead to more consistent predictive accuracy.

Business

Scenario:

The sales forecasting figures seem a bit skewed. It might be helpful to reevaluate the statistics behind our model.

Response:

I couldn't agree more. We should utilize more exhaustive cross-validation methods on our sales model to ensure its statistical integrity.