Machine Learning: Random Forest with Python from Scratch - Accuracy and Error-2
Interactive Video
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Information Technology (IT), Architecture, Social Studies, Mathematics
•
University
•
Practice Problem
•
Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the initial step in defining the accuracy function?
Setting the correct count to zero
Calculating the total number of test data
Printing the accuracy
Converting keys to a list
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of string interpolation in the accuracy function?
To display the actual and predicted values
To calculate the accuracy
To convert keys to a list
To iterate over test data
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is accuracy calculated in the function?
By subtracting incorrect predictions from total values
By adding correct count to total values
By dividing correct count by total values and multiplying by 100
By multiplying correct count by 100
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the accuracy of the single decision tree mentioned in the video?
70%
80%
85%
75%
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key benefit of using a random forest over a single decision tree?
It uses fewer computational resources
It is easier to implement
It requires less data
It reduces the chance of error
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the next step after creating a single decision tree?
Creating another tree with different data
Combining the tree with a support vector machine
Testing the tree on the same data
Implementing a neural network
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How are different trees in a random forest trained?
On randomly selected features
On the same subset of data
On different subsets of data
On the entire dataset
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