Skip to content

CS Professional · Artificial Intelligence, Data Analytics and Cyber Security - Laws and Practice · Artificial Intelligence - Introduction and Basics

A model for detecting fraudulent UPI transactions scores almost perfectly on its training data but performs poorly on fresh transactions it has not seen. What is the most accurate description of this problem?

This is overfitting. The model has memorised the details and noise of its training data, so it scores very well there but fails to generalise to new transactions. Underfitting, by contrast, would show poor performance on training data as well.

  1. AUnderfitting, because the model is too simple to learn patterns
  2. BOverfitting, because the model has memorised noise in the training dataCorrect
  3. CData encryption failure in the training pipeline
  4. DReinforcement penalty caused by too many rewards

Explanation

High accuracy on training data but poor accuracy on unseen data is the hallmark of overfitting: the model has learned noise and specifics instead of general patterns. Underfitting would show poor results on both training and new data.

Did you get it right without looking?

One question tells you little. A timed set on Artificial Intelligence - Introduction and Basics shows your real accuracy, how long you take and where you lose marks.

Practise Artificial Intelligence - Introduction and BasicsFree diagnostic mock

More Artificial Intelligence - Introduction and Basics questions