
Exploring Regression and Machine Learning
Quiz
•
Information Technology (IT)
•
University
•
Practice Problem
•
Easy
Sunder Rajini
Used 1+ times
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is regression in the context of machine learning?
Regression is a method for predicting continuous outcomes in machine learning.
Regression is a method for optimizing binary outcomes.
Regression is a technique for clustering similar data points.
Regression is used for classifying categorical data.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Name two common types of regression techniques.
Linear Regression, Logistic Regression
Time Series Regression
Ridge Regression
Polynomial Regression
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of regression evaluation metrics?
To determine the best features for a model
The purpose of regression evaluation metrics is to measure the accuracy and effectiveness of regression models.
To visualize data distributions
To optimize the training speed of models
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
List three common regression evaluation metrics.
Mean Absolute Error, Mean Squared Error, R-squared
Precision Score
F1 Score
Accuracy Score
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is unsupervised learning?
Unsupervised learning is a machine learning approach that finds patterns in data without labeled outputs.
A process that eliminates noise from data before analysis.
A technique used only for classification tasks.
A method that requires labeled data for training.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does clustering differ from classification?
Clustering uses predefined categories, while classification does not.
Clustering is a supervised learning method, while classification is unsupervised.
Clustering requires labeled data, while classification does not.
Clustering groups data without labels, while classification assigns labels based on known categories.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main goal of clustering in unsupervised learning?
To group similar data points together.
To predict future outcomes based on historical data.
To reduce the dimensionality of the data.
To classify data points into predefined categories.
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