10 Questions
Why is it not recommended to use the same data to evaluate a model that was used to train it?
The model will overfit and memorize the training data
Which term describes the process of understanding the reliability of an AI model by comparing its outputs with actual answers?
Model Evaluation
What is a common risk associated with evaluating a model solely based on its performance on the training dataset?
Model will overfit and fail to generalize well
What does underfitting in a model indicate?
The model's accuracy is lower and it fails to capture the true function
How is overfitting in a model defined?
The model is trying to cover all the data samples even if they are out of alignment to the true function
Why is evaluating AI models important?
To understand the efficiency and accuracy of the model
Why is Precision considered an important evaluation criteria for models?
Precision helps in reducing false alarms and increasing true positive cases.
What does high Precision imply about a model's performance?
High Precision indicates more true positive cases and fewer false alarms.
In the context of the text, what might happen if Precision is low?
There will be more false alarms than actual fires.
Why is good Precision not equivalent to good model performance?
Good Precision can lead to complacency and missed opportunities.
Test your knowledge on model evaluation techniques which are used to find the best model for representing data and predicting future outcomes. Learn about the process of evaluating AI models by comparing their outputs with actual answers using test datasets.
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