Tuesday, 12 May 2020

Machine Learning Exam Questions TRUE or FALSE 01

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


Machine Learning TRUE / FALSE Questions - SET 01


1. Stochastic gradient descent performs less computation per update than batch gradient descent.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE
Stochastic gradient descent (SGD) computes the gradient using a single sample. For example, if the training set contains 100 samples then the parameters are updated 100 times that is one time after every individual example is passed through the network.
Batch gradient descent computes the gradient using the whole dataset. For example, if the training dataset contains 100 training examples then the parameters of the neural network are updated once.

2. To classify job applications into two categories and to detect the applicants who lie in their applications using density estimation to detect outliers we can use generative classifiers.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE
For the purpose of density estimation, we need to calculate P(x|y). Hence, we can use generative classifiers.

3. A good way to pick the number of clusters k, used for k-Means clustering is to try multiple values of k and choose the value that minimizes the distortion measure.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE
Large K may be good for feature representations, but smaller K may be more interpretable. Unfortunately, there is no single best way to determine K from the data.
As the value of K increases, there will be fewer elements in the cluster. So average distortion will decrease.
To find a satisfactory clustering result, usually, a number of iterations are needed where the user executes the algorithm with different values of K. The validity of the clustering result is assessed only visually without applying any formal performance measures. With this approach, it is difficult for users to evaluate the clustering result for multi-dimensional data set

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