ML QUIZ1

In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize?

• (h(x) – y) / m

• (h(x) – y)² / 2*m

• (h(x) – y) / 2*m

• (y – h(x))

Can a cancer detection problem be solved by logistic regression?

• Yes, logistic regression can be used to solve a cancer detection problem.
• Cancer detection requires complex neural networks, not logistic regression.
• Logistic regression can only be used for binary classification problems unrelated to health.
• No, logistic regression is not suitable for cancer detection.

In a logistic regression problem, what is a possible output for a new instance?

• A continuous value greater than 1
• A binary value of 0 or 1
• A categorical label such as 'yes' or 'no'
• A probability value between 0 and 1

Let g be the sigmoid function. Let a = 0. What is the value of g(a)?

• -1
• 1
• 0.5
• 0

What is the Manhattan distance between a data point (9, 7) and a new query instance (3, 4)?

• 9
• 5
• 12
• 8

Which of the following statements is not true about the Decision tree?

• It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training set

• It performs a series of iterations and on each iteration, it examine the effect of splitting a single leaf
  

• It defines some gain mea sure that quantifies the improvement due to the split

• Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all

Which of the following statements is not true about Information Gain?

• It is a gain measure that is used in the ID3 algorithms

• It is the difference between the entropy of the label before and after the split

• It is based on the decrease in entropy after a data-set is split on an attribute

• Constructing a decision tree is all about finding attribute that returns the lowest information gain

Given the entropy for a split, E_split = 0.39 and the entropy before the split, E_before = 1. What is the Information Gain for the split?

• 0.75
• 1.0
• 0.61
• 0.39

Given entropy of parent = 1, weights averages = (3/4,1/4) and entropy of children = (0.9, 0). What is the information gain?

• 0.1
• 0.5
• 0.325
• 0.75

K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?

• Instance-based learning
  
  

• Parametric learning

• Model-based learning

• Unsupervised learning