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Quiz on Random Forest

Quiz on Random Forest

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Question 1 of 10

1. Question

What is a Random Forest?

A single decision tree
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A clustering technique
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A collection of decision trees working together
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A neural network model
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Question 2 of 10

2. Question

What type of machine learning algorithm is Random Forest?

Supervised Learning
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Unsupervised Learning
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Reinforcement Learning
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Semi-supervised Learning
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Question 3 of 10

3. Question

Which of the following is true about Random Forest?

It always uses the same decision tree
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It creates multiple trees and combines their outputs
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It works only for regression problems
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It doesn’t use any randomness
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Question 4 of 10

4. Question

What technique does Random Forest use to improve accuracy?

Gradient descent
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Averaging or majority voting
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Feature scaling
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Dimensionality reduction
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Question 5 of 10

5. Question

What is the main reason for using multiple trees in Random Forest?

To reduce model complexity
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To overfit the data
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To reduce overfitting and improve performance
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To save memory
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Question 6 of 10

6. Question

How does Random Forest introduce randomness?

By using a random activation function
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By selecting random data points and random features for each tree
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By randomly deleting trees
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By shuffling the output values
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Question 7 of 10

7. Question

Random Forest is considered an ensemble method because:

It combines models to make better predictions
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It uses deep learning networks
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. It reduces the number of features
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It only uses a single model
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Question 8 of 10

8. Question

What happens when the number of trees in a Random Forest increases?

The model becomes less accurate
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The model always overfits
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The model becomes more stable and accurate (to a limit)
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The model starts deleting old trees
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Question 9 of 10

9. Question

Which of the following is NOT a benefit of Random Forest?

Handles missing values well
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Works with both classification and regression
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Requires heavy feature engineering
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Reduces overfitting compared to a single decision tree
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Question 10 of 10

10. Question

In Random Forest, how is the final prediction made in classification problems?

By choosing the smallest output value
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By averaging the outputs of all trees
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By taking the majority vote from all trees
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By using the first tree’s result
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