ML-Quiz#2

A computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T, as measured by P, improves with experience E. Suppose we feed a learning algorithm a lot of historical weather data, and have it learn to predict weather. What would be a reasonable choice for P?

• The probability of it correctly predicting a future date’s weather.

• The weather prediction task.

• The process of the algorithm examining a large amount of historical weather data.

• None of these.

Some of the problems below are best addressed using a supervised learning algorithm, and the others with an unsupervised learning algorithm. Which of the following would you apply supervised learning to? (Select all that apply.) In each case, assume some appropriate dataset is available for your algorithm to learn from.

• Given historical data of children’s ages and heights, predict children’s height as a function of their age.

• Given 50 articles written by male authors, and 50 articles written by female authors, learn to predict the gender of a new manuscript’s author (when the identity of this author is unknown).

• Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number of groups of essays that are somehow “similar” or “related”.

• Examine a large collection of emails that are known to be spam email, to discover if there are sub-types of spam mail.

Some of the problems below are best addressed using a supervised learning algorithm, and the others with an unsupervised learning algorithm. Which of the

following would you apply unsupervised learning to? (Select all that apply.) In each case, assume some appropriate dataset is available for your algorithm to learn from.

• Have a computer examine an audio clip of a piece of music, and classify whether or not there

  are vocals (i.e., a human voice singing) in that audio clip, or if it is a clip of only musical

  instruments (and no vocals).

• Given genetic (DNA) data from a person, predict the odds of him/her developing diabetes over

  the next 10 years.

• Given a large dataset of medical records from patients suffering from heart disease, try to

  learn whether there might be different clusters of such patients for which we might tailor

  separate treatments.

• To perform market basket analysis by finding association between the items bought at the

  grocery store.

Given the transactions in Table 1, what is the confidence of the association rule

{Diapers} ⇒ {Coffee, Nuts}?

• 0.4

• 0.5

• 0.6

• 0.8

Consider a image classifier problem. The test set contain 10 images. Assume that the ML algorithm predicted 7 images correctly. What is the accuracy of the classifier?

• 0.7

• 0.6

• 0.3

• None of the answer

Which one of the following functions best describes relation between input and output, given the data distribution is parabolic in nature?

• y = ax + c

• y = ax^2 + bx + c

• y = ax^3 + bx^2 + cx

• None of the answers

what will be Mean Squared Loss value, given any model y = f(x) ?

• Positive value

• 0

• any non negative value

• 1

Learning the parameter for models can be formulated as which of these problems?

• Optimization problem

• Graph search problem

• Brute force search problem

• Binary algebraic problem

"For any given a ML Task the Evaluation metric and Loss metric should be same."

• True

• False