General MCQ for Machine Learning

1. ln machine learning, logistic regression:

2. Statement 1: Reinforcement learning is an off-line technique. Statement 2: The reinforcement learning technique is used in elevator scheduling.

3. You work for a music streaming service and want to use supervised machine learning to classify music into different genres. Your service has collected thousands of songs in each genre, and you used this as your training data. Now you pull out a small random subset of all the songs in your service. What is this subset called?

4. Your university wants to use machine learning algorithms to help sort through incoming student applications. An administrator asks if the admissions decisions might be biased against any particular group, such as women. What would be the best answer?

5. You want to create a supervised machine learning system that identifies pictures of kittens on social media. To do this, you have collected more than 100,000 images of kittens. What is this collection of images called?

6. Naive Bayes looks at each _ predictor and creates a probability that belongs in each class.

7. Your supervisor asks you to create a machine learning system that will help your human resources department classify jobs applicants into well-defined groups. What type of system are you more likely to recommend?

8. You work for a website that helps match people up for lunch dates. The website boasts that it uses more than 500 predictors to find customers the perfect date, but many costumers complain that they get very few matches. What is a likely problem with your model?

9. The activations for class A, B and C before softmax were 10,8 and 3. The different in softmax values for class A and class B would be :

10. You create a decision tree to show whether someone decides to go to the beach. There are three factors in this decision: rainy, overcast, and sunny. What are these three factors called?

11. You want to create a machine learning algorithm to identify food recipes on the web. To do this, you create an algorithm that looks at different conditional probabilities. So if the post includes the word flour, it has a slightly stronger probability of being a recipe. If it contains both flour and sugar, it even more likely a recipe. What type of algorithm are you using?

12. What is Q-learning reinforcement learning?

13. Your machine learning system is using labeled examples to try to predict future data, compare that data to the predicted result, and then the model. What is the best description of this machine learning method?

14. You are working with your machine learning algorithm on something called class predictor probability. What algorithm are you most likely using?

15. Your data science team is often criticized for creating reports that are boring or too obvious. What could you do to help improve the team?

16. An organisation that owns dozens of shopping malls wants to create a machine learning product that will use facial recognition to identify customers. What is the main challenge of developing such a model?