Page 310 - Ai Book - 10

Ans. True Positives signify instances where the model correctly predicts a positive outcome. In scenarios
like medical diagnoses or disaster predictions, a True Positive means the model accurately identified
a condition or event, showcasing its capability to make correct positive predictions, which is crucial for
decision-making in such critical situations.

4. Explain on the role of a confusion matrix in evaluating the performance of an AI model.
Ans. A confusion matrix is a table that summarizes the model’s prediction results, including True Positive,
True Negative, False Positive, and False Negative. This matrix provides a comprehensive overview of how
well the model performs on different types of predictions, aiding in assessing its strengths and areas
for improvement. It serves as a valuable tool for understanding the distribution of outcomes and the
model’s overall effectiveness.
5. How is Precision calculated, and why is it considered a critical parameter in model evaluation?
Ans. Precision is calculated as the ratio of True Positive to the sum of True Positive and False Positive. It
is crucial because it measures the accuracy of positive predictions. High precision indicates that the
model is making fewer false positive predictions, which is particularly important in applications where
inaccurate positive predictions can have significant consequences, such as predicting floods or water
shortages.

G. Application based question.
1. Imagine you have been assigned a task to deploy an AI-based flood prediction model in a region prone
to flooding. How would you evaluate the efficiency of the model using concepts like True Positive, True
Negative, False Positive, and False Negative? Provide a scenario-based explanation.
Ans. To evaluate the flood prediction model, we would assess its predictions based on real conditions. True
Positives would be instances where the model correctly predicts flooding when it occurs. True Negative
would represent cases where the model accurately predicts no flooding, and there is no flooding. False
Positive occur when the model incorrectly predicts flooding when there is none, and False Negative occur
when the model misses predicting flooding when it happens. The evaluation would involve analysing
these outcomes to gauge the model’s accuracy and reliability in flood predictions.
2. Consider deploying an AI model for medical diagnoses. How can you apply Precision, Recall, and F1
Score in the context of diagnosing a specific medical condition? Provide an example to illustrate the
significance of these metrics.
Ans. In medical diagnoses, Precision would measure the accuracy of positive predictions, such as correctly
identifying individuals with a specific medical condition. Recall would assess the model’s ability to
capture all true positive cases among those individuals who actually have the condition. F1 Score, being
a harmonic mean of Precision and Recall, ensures a balance between minimizing false positives and false
negatives. For instance, in diagnosing a rare disease, high Precision would mean fewer misdiagnoses,
while high Recall would ensure capturing most actual cases, and a balanced F1 Score would be essential
for overall model effectiveness.
3. Suppose you are implementing an AI model to predict water shortages in schools. How would you
interpret True Negative in the context of this application, and why are they significant for evaluating the
model’s performance?

Ans. In the context of predicting water shortages in schools, True Negative would represent instances where
the model correctly predicts no water shortage, and indeed, there is no water shortage. These cases are
crucial as they indicate the model’s ability to accurately identify situations where the predicted negative
outcome aligns with the actual condition. High True Negative demonstrate the model’s specificity and its
proficiency in avoiding false alarms, making it an essential factor in assessing the overall reliability of the
water shortage prediction model.

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