
Exploring Machine Learning Concepts

Quiz
•
English
•
12th Grade
•
Medium
Dr.Makineedi Rajababu
Used 4+ times
FREE Resource
15 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Which of the following is a supervised learning algorithm?
Support Vector Machine
Principal Component Analysis
Linear Regression
K-Means Clustering
2.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the primary goal of unsupervised learning?
To enhance the accuracy of labeled datasets.
To identify patterns or structures in data without labeled responses.
To classify data into predefined categories.
To predict future outcomes based on past data.
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
How do you evaluate the performance of a classification model?
Use only the training accuracy to evaluate performance.
Focus solely on the model's runtime efficiency.
Use metrics like accuracy, precision, recall, F1 score, and confusion matrix.
Ignore the confusion matrix and only consider ROC curves.
4.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the difference between classification and regression tasks?
Classification requires more data than regression tasks.
Classification predicts numerical values; regression predicts categories.
Classification predicts categories; regression predicts continuous values.
Classification is used for time series; regression is for image analysis.
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Which method is commonly used for statistical learning?
Data mining
Machine learning
Qualitative analysis
Regression analysis
6.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is a decision tree used for in machine learning?
A decision tree is primarily for data storage.
A decision tree is used for image processing tasks.
A decision tree is used for natural language generation.
A decision tree is used for classification and regression tasks in machine learning.
7.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
How does the k-nearest neighbors (KNN) algorithm work?
KNN classifies a data point based on the majority label of its k nearest neighbors.
KNN requires a predefined model to classify data points.
KNN uses a decision tree to classify data points.
KNN predicts a data point based on a weighted average of all data points.
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