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WorksheetsFQuiz-NLP/NaiveBayes-AI
Total questions: 35
Worksheet time: 35mins
What does NLP stand for?
Natural Language Processing
Neural Language Processing
Neural Language Programming
Natural Learning Process
Which of the following is a common application of NLP?
Web hosting
Spam filters
Video editing
Graphic design
What is one of the challenges of language acquisition for computers?
Complexity of unstructured data
Lack of unstructured data
Simplicity of human language
Strict rules in language
Which technique is used for sentiment analysis in NLP?
Aspect extraction
Text summarization
Deep learning
Named entity recognition
What is the purpose of intelligent assistants like Siri and Alexa?
To play music
To identify speech patterns and infer meaning
To send emails
To manage calendars
What does BERT stand for in NLP?
Bidirectional Encoder Representations from Transformers
Binary Encoding Representation Technique
Basic Encoding for Real-time Translation
Bidirectional Encoding for Real-time Text
Which of the following is NOT a feature of NLP?
Autocorrect
Image processing
Text summarization
Voice recognition
What is the main function of messaging filters in NLP?
To enhance video quality
To manage user accounts
To identify unwanted messages
To improve sound quality
Which model is associated with Google AI for NLP?
GPT-3
BERT
RoBERTa
XLNet
What is a common use of translation tools in NLP?
To manage databases
To edit videos
To provide accurate translations
To create graphics
What does text analytics do?
Generates audio from text
Creates graphics from text
Converts unstructured textual data into analyzable data
Converts structured data into text
What is the role of algorithms in NLP?
To analyze the meaning and structure of words
To create graphics
To manage databases
To enhance video quality
What is one of the first applications of NLP?
Spam filters
Voice recognition
Text summarization
Data visualization
Which of the following is a challenge for translation services?
Strict rules in languages
Direct translation of all languages
Lack of data
Different sentence constructions in languages
What is the impact of BERT on search queries?
It has no impact
It improves understanding of complex searches
It is used for video processing
It only works with short queries
What type of algorithm is Naive Bayes?
Regression
Clustering
Classification
Dimensionality Reduction
Naive Bayes is based on which theorem?
Bayes' Theorem
Central Limit Theorem
Pythagorean Theorem
Law of Large Numbers
In Naive Bayes, the term "naive" refers to:
Ignorance of prior probabilities
Simplification of dependent features
Strong independence assumptions among features
Use of simple math
Bayes' theorem involves which of the following?
Conditional probabilities
Mean and variance
Confidence intervals
Decision trees
What does Naive Bayes assume about input features?
They are linearly dependent
They are normally distributed
They are conditionally independent
They are binary
Which of the following is NOT a variant of Naive Bayes?
Gaussian Naive Bayes
Multinomial Naive Bayes
Bernoulli Naive Bayes
Logistic Naive Bayes
Which Naive Bayes variant is best for text classification with word counts?
Gaussian
Multinomial
Bernoulli
Logistic
Bernoulli Naive Bayes is suitable for features that are:
Continuous
Ordinal
Binary
Categorical
Gaussian Naive Bayes is used when the features are:
Binary
Discrete
Count-based
Continuous and normally distributed
In Naive Bayes, what does the prior probability P(C) represent?
Likelihood of the data
Probability of the class before observing features
Probability of the features
Conditional probability of class given features
What is the primary advantage of Naive Bayes?
High accuracy
Handles missing values natively
Fast training and prediction
Works well with multicollinearity
Which metric is commonly used to evaluate Naive Bayes performance?
Mean Squared Error
R-squared
Accuracy
Adjusted R-squared
A drawback of Naive Bayes is that it:
Works only on binary classification
Is computationally expensive
Assumes feature independence, which is rarely true
Needs large amounts of training data
Naive Bayes is considered a generative model because:
It models P(y|x) directly
It models the decision boundary
It models how data is generated by estimating P(x|y)
It does not use probability distributions
Laplace smoothing is used in Naive Bayes to:
Normalize the data
Prevent zero probability for unseen features
Increase model accuracy
Ensure Gaussian distribution
Which of the following is NOT true about Naive Bayes?
It can be used for spam detection
It works well with high-dimensional data
It performs poorly with correlated features
It always gives the most accurate results
The likelihood term in Bayes' theorem for Naive Bayes is:
P(C)
P(X|C)
P(C|X)
P(X)
In text classification, which Naive Bayes model is generally preferred?
Gaussian
Multinomial
Bernoulli
Both B and C depending on feature format
Which preprocessing step is crucial for Naive Bayes in NLP tasks?
Feature scaling
Removing outliers
Tokenization and vectorization
Dimensionality reduction
Naive Bayes classifiers are particularly suitable when:
Features are dependent
Dataset has many categorical features
You need a non-probabilistic model
Data is highly imbalanced
