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

For statistical analysis, 'cross-validation' helps in assessing how the results of a statistical model will generalize to an independent data set. A statistician might explain, 'Cross-validation is crucial for verifying the reliability of our statistical inferences and ensuring they are not just a result of peculiarities in the sample.'

Practice Scenarios

Scenario:

The AI bot isn't generalizing well to new scenarios. We may need to retrain it with a broader range of data.

Response:

Exactly, we should use cross-validation as part of our AI training. This should make our bot more adaptable to new data scenarios.

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.