Page 207 - AI Computer 10

• Weight for car availability = 1.0

• Weight for driver experience = 1.0
• Bias = -3.0
• Threshold = 2.5
Scenarios:
• Customer 1: Car condition = 1, driver age = 1, car availability = 1, driver experience = 1.

• Customer 2: Car condition = 0, driver age = 1, car availability = 1, driver experience = 0.
b. Context: Approving an event for a venue
Factors:

• Venue Capacity: 1 if it exceeds 100 people, 0 if less
• Event Type: 1 if formal, 0 if informal
• Budget: 1 if above $5000, 0 if below
• Duration: 1 if longer than 4 hours, 0 if shorter
Weights and Bias:

• Weight for capacity = 2.0
• Weight for event type = 1.5
• Weight for budget = 2.5

• Weight for duration = 1.0
• Bias = -4.0
• Threshold = 3.0
Scenarios:
• Event 1: Capacity = 1, Event Type = 1, Budget = 1, Duration = 1.

• Event 2: Capacity = 0, Event Type = 0, Budget = 0, Duration = 0.
G. Assertion and Reason-based questions.
In the following questions, a statement of Assertion (A) is followed by a statement of Reason (R). Study both
the statements and give the answer as:
(a) if both Assertion and Reason are correct and Reason is the correct explanation of Assertion.

(b) if both Assertion and Reason are correct but Reason is not the correct explanation of Assertion.
(c) if Assertion is True but Reason is False.
(d) if Assertion is False but Reason is True.
1. Assertion (A): Deep Learning models require large amounts of labeled data for effective training.
Reason (R): Deep Learning models have multiple layers of neurons that learn hierarchical representations
from data.
2. Assertion (A): Unsupervised learning is useful for customer segmentation.
Reason (R): Unsupervised learning algorithms do not require labeled data and can discover hidden
patterns in data.
3. Assertion (A): A classification model can be used to predict the price of a house.

Reason (R): Classification models are designed to predict categorical outputs, while house price prediction
requires continuous value prediction.

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