Introduction to Machine Learning Quiz_1

Which of the following are supervised learning problems (Multiple Correct)?

• Clustering Spotify users based on their listening history

• Weather forecast using data collected by a satellite

• Predicting tuberculosis using patient’s chest X-Ray

• Training a humanoid to walk using a reward system

Which of the following are regression tasks (Multiple Correct)?

• Predicting the outcome of an election

• Predicting the weight of a giraffe based on its weight

• Predicting the emotion conveyed by a sentence

• Identifying abnormal data points

Which of the following are classification tasks (Multiple Correct)?

• Predicting the outcome of an election

• Predicting the weight of a giraffe based on its weight

• Predicting the emotion conveyed by a sentence

• Identifying abnormal data points

Which of the two functions overfit the training data?

• Both functions F1 & F2

• Function F1

• Function F2

• None of them

Which of the following 2 functions will yield higher training error?

• Function F1

• Function F2

• Both functions F1 & F2 will have the same training error

• Can not be determined

What does the term ‘policy’ refer to in reinforcement learning?

• A set of rules governing the environment

• The reward function

• The initial state of the environment

• The strategy the agent follows to choose actions

Given the following dataset, fork= 3, use KNN regression to find the prediction for a new data-point (2,3)(Use Euclidean distance measure for finding closest points)

• 2.0

• 2.6

• 2.8

• 3.2

For any given dataset, comment on the bias of K-nearest classifiers upon increasing the value of K.

• The bias of the classifier decreases

• The bias of the classifier does not change

• The bias of the classifier increases

• Can not be determined

Bias and variance are given by:

• E[ˆf(x)]−f(x),E((E[ˆf(x)]−ˆf(x))^2)

• E[ˆf(x)]−f(x),E((E[ˆf(x)]−ˆf(x)))^2

• (E[ˆf(x)]−f(x))^2,E((E[ˆf(x)]−ˆf(x))^2)

• (E[ˆf(x)]−f(x))^2,E((E[ˆf(x)]−ˆf(x)))^2

Which of the following statements are FALSE regarding bias and variance?

• Models which overfit have a high bias

• Models which overfit have a low bias

• Models which underfit have a high variance

• Models which underfit have a low variance