History Of HMM-Based Machine Learning Quiz
Discover the fascinating journey of HMM-Based Machine Learning through our engaging quiz on its historical evolution. In this fascinating quiz, you'll explore the origins of Hidden Markov Models (HMMs) and their application in various fields like Natural Language Processing, Image Recognition, and Robotics. Delve into the concept of "hidden" states and understand how HMMs model emission probabilities using Gaussian distributions.
Test your knowledge of essential algorithms like Viterbi and Baum-Welch, uncovering their significance in HMM-based model training. Whether you're a novice or an expert, this quiz offers an exciting opportunity to learn and reminisce about the captivating history of HMM-Based Machine Read moreLearning.
- 1.
What does HMM stand for in the context of machine learning?
- A.
High-Memory Modeling
- B.
Hidden Markov Model
- C.
Hierarchical Model Mapping
- D.
Hyper-Machine Modeling
Correct Answer
B. Hidden Markov Model -
- 2.
HMM-based machine learning is commonly used in which field?
- A.
Natural Language Processing
- B.
Image Recognition
- C.
Robotics
- D.
All of the above
Correct Answer
D. All of the above -
- 3.
In an HMM, what does the term "hidden" refer to?
- A.
The model's parameters
- B.
The true state or process
- C.
The features extracted
- D.
The output labels of the model
Correct Answer
B. The true state or process -
- 4.
Which probability distribution is typically used to model the emission probabilities in an HMM?
- A.
Gaussian distribution
- B.
Uniform distribution
- C.
Exponential distribution
- D.
Poisson distribution
Correct Answer
A. Gaussian distribution -
- 5.
In an HMM, what is the purpose of the transition probabilities?
- A.
Determine likelihood
- B.
Model probability of transition
- C.
Model distribution of data
- D.
Estimate number of hidden states
Correct Answer
B. Model probability of transition -
- 6.
The Viterbi algorithm is used for what task in HMM-based machine learning?
- A.
Model training
- B.
Sequence alignment
- C.
Parameter estimation
- D.
Decoding the most likely sequence
Correct Answer
D. Decoding the most likely sequence -
- 7.
Which of the following is NOT a limitation of HMM-based machine learning?
- A.
Difficulty in long-range dependencies
- B.
Lack of flexibility
- C.
Inability to model non-linear relationships
- D.
High computational complexity
Correct Answer
C. Inability to model non-linear relationships -
- 8.
HMM-based machine learning finds applications in which of the following areas?
- A.
Weather forecasting
- B.
Speech recognition
- C.
Stock market prediction
- D.
All of the above
Correct Answer
D. All of the above -
- 9.
What is the main advantage of using HMMs in time series data analysis?
- A.
Minimal computation power
- B.
Handle missing data effectively
- C.
Automatically discover features
- D.
Immune to overfitting issues
Correct Answer
B. Handle missing data effectively -
- 10.
Which algorithm is commonly used for training the parameters of an HMM?
- A.
Support Vector Machine (SVM)
- B.
K-Means clustering
- C.
Expectation-Maximization (EM)
- D.
Gradient Descent
Correct Answer
C. Expectation-Maximization (EM) -
- 11.
In the context of HMM-based machine learning, what does the "forward-backward" algorithm calculate?
- A.
Likelihood of observed sequence
- B.
Most likely sequence of hidden states
- C.
Expected counts of state transitions
- D.
Marginal probabilities of hidden states
Correct Answer
A. Likelihood of observed sequence -
- 12.
What is the purpose of the Baum-Welch algorithm in HMM-based machine learning?
- A.
Unsupervised learning of model parameters
- B.
Feature extraction
- C.
Clustering of hidden states
- D.
Model generalization
Correct Answer
A. Unsupervised learning of model parameters -
- 13.
Which statistical concept is central to the functioning of HMMs?
- A.
Variance
- B.
Entropy
- C.
Markov property
- D.
Skewness
Correct Answer
C. Markov property -
- 14.
Which of the following statements about HMMs is true?
- A.
HMMs are deterministic models.
- B.
HMMs are always overfit to the data.
- C.
HMMs can be used for classification tasks only.
- D.
HMMs are probabilistic models.
Correct Answer
D. HMMs are probabilistic models. -
- 15.
What are the two main problems associated with HMMs that the HMM-based machine learning tries to address?
- A.
Underfitting and overfitting
- B.
Bias and variance
- C.
Computation speed and memory usage
- D.
Decoding and learning the model parameters
Correct Answer
D. Decoding and learning the model parameters -
Quiz Review Timeline +
Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.
-
Current Version
-
Jul 28, 2023Quiz Edited by
ProProfs Editorial Team -
Jul 26, 2023Quiz Created by
Smriti Singh
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