Advanced Database Management System - Tutorials and Notes: Machine Learning Exam Questions TRUE or FALSE 13

Wednesday, 10 June 2020

Machine Learning Exam Questions TRUE or FALSE 13

Machine learning quiz questions TRUE or FALSE with answers, important machine learning interview questions for data science, Top 3 machine learning question set, ML exam questions


Machine Learning TRUE / FALSE Questions - SET 13

1. A cumulative distribution function (CDF) cannot be less than 0 or bigger than 1.

(a) TRUE                                                   (b) FALSE

Answer: TRUE

It is a probability function. It can be in the range of 0 to 1.

Cumulative Distribution Function (CDF) is a method to describe the distribution of random variables. It can be used to define different kinds of random variables including discrete, continuous, and mixed.

 

2. K-Means Clustering is guaranteed to converge (i.e., terminate).

(a) TRUE                                                   (b) FALSE

Answer: TRUE

K-means clustering is guaranteed to converge to a local minimum.

Since the loss function is non-negative, the k-means algorithm will eventually converge when the loss function reaches its (local) minimum.

 

3. Nearest neighbors is a parametric method.

(a) TRUE                                                   (b) FALSE

Answer: FALSE

Nearest neighbors is a non-parametric method.

That is, the method can be used even when the variables are categorical.

 

4. K-medoids is a kind of agglomerative clustering.

(a) TRUE                                                   (b) FALSE

Answer: FALSE

K-medoids is a partitioning clustering algorithm.

 

5. Performing K-nearest neighbors with K = N yields more complex decision boundaries than 1-nearest neighbor.

(a) TRUE                                                   (b) FALSE

Answer: FALSE

In k nearest neighbors, k=1 increases the complexities.

Increasing “k” simplifies decision boundary.

K = 1 (complex)

K = N: always predict majority class in dataset. (simple)

As k increases, we are averaging over more neighbors–the effective decision boundary is more “smooth”.

 


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