Machine Learning Mcq

Machine Learning MCQ: Test Your Knowledge!

 

 

What are regression algorithms in machine learning used for?
Predicting continuous outcomes
Classifying data into categories
Reducing dimensionality
Grouping similar items together
What is machine learning bagging?
A technique to reduce variance by training multiple models on different subsets of data
A method to increase bias in models
A clustering algorithm
A technique to reduce the number of features
What is a common use case for time series forecasting?
Predicting stock prices
Clustering customers
Classifying images
Reducing data dimensionality
What is the main advantage of using open-source LLMs for commercial purposes?
They are always more accurate than proprietary models
They allow for customization and transparency
They don't require any training data
They are always faster than proprietary models
What is the main purpose of a decision tree in machine learning?
To visualize data
To make a series of decisions based on input features
To reduce model complexity
To increase training speed
What are applications for machine learning in automotive industry?
Only for manufacturing
Only for design
Autonomous driving, predictive maintenance, and more
It's not used in automotive industry
Why is it important to understand different machine learning algorithms?
To avoid using data
To ensure the model fails
To choose the right tool for the problem
To avoid testing data
What is an example of machine learning applications?
Fraud detection in finance
Manually entering data into spreadsheets
Sending automated emails
Hardcoding website designs
What is the main difference between cosine similarity and Euclidean distance?
Cosine similarity is only for text data
Cosine similarity measures angle, Euclidean distance measures magnitude
Euclidean distance is only for high-dimensional data
There is no difference
What does the term "overfitting" describe in machine learning?
A model that performs well on training data but poorly on unseen data
A model that performs well on unseen data but poorly on training data
A model that generalizes well across datasets
A method to reduce model bias
Machine learning is a subset of?
Artificial intelligence
Statistics
Data mining
Mathematics
What is a perceptron in machine learning?
A basic unit of a neural network
A type of clustering algorithm
A data visualization tool
A method for dimensionality reduction
What is the main function of stop words in NLP?
To stop the processing of text
To filter out common words that add little meaning
To indicate the end of a sentence
To prevent offensive language
Examples of artificial intelligence and machine learning?
Image recognition and voice assistants
Manual data entry and rule-based systems
File management and word processing
Static websites and hard-coded logic
What is the main function of clustering algorithms in machine learning?
Grouping similar data points together
Classifying data into predefined categories
Predicting continuous outcomes
Reducing data dimensionality
What is the purpose of boosting in machine learning?
To combine weak models to create a strong model
To reduce the size of the dataset
To cluster similar data points
To clean and preprocess data
What is the purpose of the Fourier transform in signal processing and machine learning?
To perform classification
To convert signals between time and frequency domains
To reduce dimensionality
To generate synthetic data
What is the purpose of feature scaling?
To normalize the range of features for a model
To increase the size of the dataset
To reduce the number of features
To cluster similar data points
What is the primary advantage of using deep learning for chatbots?
They never make mistakes
They can understand and generate more natural language
They don't require any training data
They are always faster than rule-based chatbots
What is the main purpose of hyperparameter tuning in machine learning?
To increase the size of the training dataset
To optimize model performance
To reduce the number of features
To eliminate overfitting completely
Score: 0/20