Data Science and Machine Learning (Theory and Projects) A to Z - Building Machine Learning Model from Scratch: K-Means C

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary goal of K-Means clustering?
To reduce the dimensionality of data
To predict future data points
To group data into clusters without predefined labels
To classify data into predefined categories
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How are initial means selected in K-Means clustering?
They are predetermined by the user
They are chosen based on the largest data points
They are calculated as the average of all data points
They are selected randomly from the data points
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why do the initial means not need to be actual data points?
Because they are recalculated during the process
Because they are only used for visualization
Because they are not important in clustering
Because they are fixed throughout the process
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens after assigning data points to the nearest mean in K-Means?
The process stops and results are finalized
The means are recalculated based on the new groupings
The data points are removed from the dataset
The initial means are changed randomly
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
When does the K-Means algorithm typically stop iterating?
When all data points are assigned to a single cluster
After a fixed number of iterations
When the means stop changing significantly
When the user manually stops it
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a potential challenge in K-Means clustering?
It always converges to the same solution
It requires labeled data
It may not converge due to poor initial mean selection
It can only handle two-dimensional data
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a heuristic in the context of K-Means clustering?
A way to predict future data points
A technique to visualize clusters
A rule to improve initial mean selection
A method to ensure the algorithm runs faster
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