Machine Learning Random Forest with Python from Scratch - Classification versus Regression

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Information Technology (IT), Architecture
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a type of unsupervised machine learning?
Decision Trees
Clustering
Regression
Classification
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary goal of classification in machine learning?
Optimizing decision boundaries
Categorizing data into predefined classes
Predicting continuous values
Finding patterns in unlabeled data
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of classification, which of the following is an example of a categorical outcome?
Predicting house prices
Determining if an email is spam
Estimating a person's salary
Calculating the area of a plot
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does regression differ from classification in machine learning?
Regression predicts categorical outcomes, while classification predicts continuous values.
Regression and classification are both used for clustering data.
Regression is used for unsupervised learning, while classification is for supervised learning.
Regression predicts continuous values, while classification predicts categorical outcomes.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a common algorithm used for regression?
Decision Trees
Support Vector Machines
Linear Regression
Logistic Regression
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which algorithm can be used for both classification and regression tasks?
K-Means Clustering
Logistic Regression
Naive Bayes
Random Forest
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main characteristic of unsupervised machine learning?
It uses labeled data for training.
It categorizes data into predefined classes.
It predicts continuous values.
It finds patterns in unlabeled data.
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