overfitting

Vocabulary Word

Definition

'Overfitting' is a common issue in machine learning where the model is too specific to the training data. This is like studying for a test just by memorizing all the answers that might not help in different questions.

Examples in Different Contexts

In impact assessment, 'overfitting' can result in models that don't translate well to new situations. An evaluator might observe, 'By avoiding overfitting, we ensure our models remain adaptable to different scenarios and settings.'

Practice Scenarios

Business

Scenario:

Our sales forecasting for the upcoming quarter seems to be unusually high. The sudden spike in sales figures from the previous holiday season might have impacted our prediction model.

Response:

Right, we need to address the problem of overfitting in our sales forecasting model to make it more adaptable to diverse market conditions.

Tech

Scenario:

Our spam detection model has been showing increased error rates recently. It seems to be classifying some regular business correspondence emails incorrectly as spam.

Response:

Agreed. We need to avoid overfitting in our spam detection model to improve its performance across various types of emails.