Advanced Database Management System - Tutorials and Notes: Machine Learning Exam Interview Questions TRUE or FALSE 04

Wednesday, 13 May 2020

Machine Learning Exam Interview Questions TRUE or FALSE 04

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 04


1. Training neural networks has the potential problem of overfitting the training data.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE

Overfitting of the training data happens if neural network model is suffering from high variance. It means the trained parameters fits the training set well, but performs poorly when tested on “unseen” data (the training or the validation set).

Solutions:
More training data
Reducing the number of hidden layers
Increasing regularization parameter

2. A support vector machine computes P(y|x).

(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE
Support Vector Machine is a linear model for classification and regression problems. SVM is an algorithm that takes the data as an input and outputs a line that separates those classes if possible.

Objective of SVM

The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space (N — the number of features) that distinctly classifies the data points.

3. One drawback of maximum likelihood estimation is that in some scenarios (for example, multinomial distribution), it may return probability estimates of zero.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE

One drawback of Maximum Likelihood Estimation (MLE) is that in some scenarios it may return zero probability estimates. This happens when we try to evaluate MLE models on unseen data.

MLE may overfit the data: it will assign 0 probabilities to words it hasn't seen.
This may not happen with equi-probable events like coin flips, dice etc. It usually occurs in language models in Natural Language Processing.
Example:
Zero probabilities are clearly a problem in language models, such as when predicting the next word in a speech recognition application, because many words will be sparsely represented in the training data. In such cases, the next word may be unseen. Hence, this may end up in zero probability value.


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