The video tutorial introduces binary classification, using a rain prediction example to explain how probabilities are calculated for binary outcomes. It then transitions to discussing multi-class classification, using the example of identifying images as a car, bicycle, or motorbike. The tutorial highlights the differences between binary and multi-class classification and provides foundational knowledge on how to approach problems involving multiple classes.

What is binary classification primarily concerned with?

• Predicting multiple outcomes

• Predicting continuous values

• Predicting one outcome

• Predicting two possible outcomes

If there is a 70% chance of rain, what is the probability of no rain?

• 0.3

• 0.7

• 0.5

• 0.2

How can the probability of no rain be calculated if the probability of rain is known?

• By adding the probability of rain to one

• By subtracting the probability of rain from one

• By multiplying the probability of rain by two

• By dividing the probability of rain by two

What is an example of a multi-class classification problem?

• Predicting the weather

• Identifying a picture as a car, bicycle, or motorbike

• Determining if a coin flip is heads or tails

• Calculating the probability of rain

What challenge does multi-class classification address?

• Predicting binary outcomes

• Distinguishing between two outcomes

• Predicting continuous values

• Distinguishing between more than two classes