The video discusses the importance of data collection in machine learning, highlighting various survey methods such as telephonic, mail, and personal interviews, along with their advantages and disadvantages. It also covers basic sampling methods like random and systematic sampling, as well as advanced techniques like stratified and cluster sampling. The video concludes with an explanation of convenience sampling.

Why is data collection crucial in machine learning?

• It helps in reducing the size of datasets.

• It ensures the accuracy of algorithms.

• It provides the large datasets needed for training models.

• It simplifies the machine learning process.

Which of the following is NOT a method of conducting surveys?

• Mail surveys

• Online gaming

• Personal interviews

• Telephonic surveys

What is a disadvantage of telephonic surveys?

• They require physical presence.

• They are expensive.

• Some people may not answer calls.

• They provide biased results.

Why might mail surveys be more candid?

• They are anonymous.

• They are conducted in person.

• They are faster to complete.

• They are less expensive.

What is a potential flaw of personal interview surveys?

• They are too quick.

• They can be biased by the interviewer.

• They are not detailed enough.

• They are too impersonal.

What is the main characteristic of random sampling?

• Subjects are selected based on their location.

• Subjects are selected from a single group.

• Subjects are selected using chance methods.

• Subjects are selected based on convenience.

How are systematic samples obtained?

• By selecting subjects based on convenience.

• By selecting subjects from a single cluster.

• By selecting every kth subject.

• By selecting subjects randomly.

What distinguishes stratified sampling from other methods?

• It uses random numbers.

• It divides the population into strata.

• It selects subjects based on convenience.

• It uses geographic location.

In cluster sampling, how are subjects selected?

• From a single stratum.

• From every kth subject.

• From randomly selected clusters.

• From each individual in the population.

What is a key feature of convenience sampling?

• It divides the population into clusters.

• It selects subjects that are easily accessible.

• It uses systematic methods.

• It is based on random selection.