Machine Learning Methods: 10 practice questions

Question 1 of 10

What defines a supervised learning problem?

1. AThe inputs omit targets so the system must discover their internal groupings
2. BAn agent receives rewards while interacting with a changing environment
3. CThe model selects actions by balancing uncertain and known rewards
4. DThe training inputs include target values that provide known outcomes

Question 2 of 10

What defines an unsupervised learning problem?

1. AThe inputs have no labels or targets, so outputs rely on discovered patterns
2. BA model predicts one of several predefined classes from correctly labeled examples
3. CAn agent takes trial-and-error actions to maximize long-term rewards
4. DEvery input has a categorical or continuous target supplied during training

Question 3 of 10

What is the central learning mechanism in reinforcement learning?

1. AA regression model estimates a continuous target from related variables
2. BA system groups unlabeled examples once without receiving environmental feedback
3. CA model maps labeled input attributes directly to supplied target values
4. DAn agent learns through trial-and-error interaction to maximize long-term rewards

Question 4 of 10

Within supervised learning, when is a problem classification rather than regression?

1. AThe inputs have no targets and must be divided into discovered clusters
2. BThe agent receives rewards after actions in a dynamic environment
3. CThe target is categorical, such as one of several predefined classes
4. DThe target is continuous, such as an estimated house sale price

Question 5 of 10

Which task is a supervised regression problem?

1. ADiscovering customer groups from attributes when no group labels exist
2. BAssigning labeled documents to one of several predefined topic classes
3. CEstimating a house price from features when historical sale prices are supplied
4. DLearning a driving policy from actions and long-term track rewards

Question 6 of 10

What is the goal of cluster analysis in unsupervised learning?

1. AMaximize long-term rewards by selecting actions in a dynamic environment
2. BEstimate a continuous target value from features paired with known answers
3. CForm groups whose members are similar within a group and different across groups
4. DChoose between two predefined classes from correctly labeled examples

Question 7 of 10

How does reinforcement learning balance exploration and exploitation?

1. AIt trades labeled rows against unlabeled rows to create fixed cluster names
2. BIt trades training features against target columns to remove environmental feedback
3. CIt trades off actions with uncertain rewards against actions with known rewards
4. DIt trades categorical targets against continuous targets in the same regression output

Question 8 of 10

A retailer has historical examples with a product category and a numeric sale price. It must predict the category for one workflow and the price for another. Which approach fits both targets?

1. AUse supervised regression for the category and supervised classification for the price
2. BUse unsupervised clustering for the category and reinforcement learning for the price
3. CUse reinforcement learning for the category and unsupervised clustering for the price
4. DUse supervised classification for the category and supervised regression for the price

Question 9 of 10

A fraud team has transaction features but no fraud labels. It wants to discover natural groupings and flag rare observations that differ sharply from the rest. Which paradigm best matches both goals?

1. AUnsupervised learning, using clustering and anomaly detection on the unlabeled inputs
2. BSupervised learning, using regression against fraud targets already present in the data
3. CReinforcement learning, using environmental rewards to label every historical transaction
4. DSupervised classification, using predefined fraud classes that the dataset does not contain

Question 10 of 10

A simulated robot must choose sequential actions in a changing environment. Feedback arrives as rewards, and success depends on a long-term goal rather than a supplied correct action for each state. Which paradigm should be used?

1. ASupervised regression, because a continuous target supplies the correct action before interaction
2. BReinforcement learning, because the agent learns by interaction while maximizing long-term reward
3. CUnsupervised clustering, because similarity groups directly maximize delayed environmental rewards
4. DSupervised classification, because every state already includes the correct action as a target