pushpapune / HacktoberFest-2020

HacktoberFest-2020

Q. Are data model bias and variance a challenge with unsupervised learning?

• No, data model bias and variance are only a challenge with reinforcement learning.
• Yes, data model bias is a challenge when the machine creates clusters.
• Yes, data model variance trains the unsupervised machine learning algorithm.
• No, data model bias and variance involve supervised learning.

Q. Which choice is best for binary classification?

• K-means
• Logistic regression
• Linear regression
• Principal Component Analysis (PCA)

Q. With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs

• supervised learning
• data
• unsupervised learning
• algorithms

Q. Why is it important for machine learning algorithms to have access to high-quality data?

• It will take too long for programmers to scrub poor data.
• If the data is high quality, the algorithms will be easier to develop.
• Low-quality data requires much more processing power than high-quality data.
• If the data is low quality, you will get inaccurate results.

Q. You work for a large pharmaceutical company whose data science team wants to use unsupervised learning machine algorithms to help discover new drugs. What is an advantage to this approach?

• You will be able to prioritize different classes of drugs, such as antibiotics.
• You can create a training set of drugs you would like to discover.
• The algorithms will cluster together drugs that have similar traits.
• Human experts can create classes of drugs to help guide discovery.