Evaluation in AI

Evaluation in AI

• Final Result of AI model

• Process of understanding the model

• Testing the model

• Finalize the model

Prediction and ___________ are the two parameters considered for Evaluation of a model

• Reality

• Testing

• Training

• Evaluation

Models that use the training dataset during testing, will always results in correct output. This is known as _________

• Evaluation technique

• Technofitting

• Output fitting

• Overfitting

While doing the evaluation, we need to know __________ of the model.

• Prediction of data

• Accuracy of data

• Reliability of data

• Consistency of data

Correct prediction of model will have some _________

• percentage of results

• sum of results

• accuracy of result

• All of the above

False positive value means

• Type 2 error in model

• Type 1 error in model

• No error in model

• Negative error in model

When the prediction is made in model to test, it will be compared with the reality which gives the accurate result

• True

• False

The result of comparison between the prediction and reality can be recorded in what we call the

• Overfitting

• Problem Scoping

• Confusion Matrix 

• Data acquisition

_______ is defined as the percentage of true positive cases versus all the cases where the prediction is true.

• Precision

• Recall

• Accuracy

• Confusion Matrix

We can balance the prediction and reality in any model while evaluating it.

• True

• False