What is the primary purpose of the evaluation stage in an AI project cycle?

To assess the reliability of AI models by comparing their predictions with actual outcomes.

Fill in the blank: High accuracy does not necessarily indicate ___ performance in an AI model.

Good

True or False: A confusion matrix is considered an evaluation metric.

False; it is a record that aids in evaluation.

What are the four conditions that can arise during model predictions?

True Positive, True Negative, False Positive, False Negative.

In the context of model evaluation, define Precision.

Precision is the percentage of true positive cases among all predicted positive cases.

Riddle: I measure the fraction of actual positives identified correctly. What am I?

Recall

Fill in the blank: A model that predicts a fire when there isn't one is a case of ___ positive.

False

What is the relationship between Precision and False Positives?

Precision decreases as the number of False Positives increases.

True or False: Recall takes False Positives into account in its calculation.

False; Recall considers False Negatives.

Fill in the blank: The F1 Score is a measure of balance between ___ and ___.

Precision; Recall

Explain why high Precision does not always indicate a good model performance.

High Precision can occur even if there are many False Negatives, which means the model may miss important positive cases.

What is the formula for Accuracy in model evaluation?

Accuracy = (True Positive + True Negative) / Total Observations.

Riddle: I help you understand if your predictions match reality. What am I?

Confusion Matrix

Fill in the blank: In scenarios where False Negatives are costly, ___ is more important than ___.

Recall; Precision

Define the F1 Score in your own words.

The F1 Score is a harmonic mean of Precision and Recall, providing a single metric to evaluate model performance.