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Random Forest
Random Forest (Bagging Algorithm) Quiz
Question 1
What is the main advantage of using the Random Forest algorithm?
It always gives 100% accuracy
It reduces overfitting and improves generalization
It requires only one decision tree for better performance
It works only for classification problems
Question 2
How does Random Forest prevent overfitting?
By using a single deep decision tree
By selecting all features at each split
By averaging predictions from multiple trees trained on different subsets of data
By using only the most important features
Question 3
What technique does Random Forest use to create diverse decision trees?
Bootstrapping
Cross-validation
Gradient boosting
K-means clustering
Question 4
How does Random Forest select features at each split in a tree?
It considers all features every time
It uses only the most correlated features
It removes features with missing values
It selects a random subset of features
Question 5
What is a key characteristic of Random Forest?
It requires extensive data preprocessing
It works only with numerical data
It is a supervised learning algorithm
It cannot handle missing values
There are 5 questions to complete.
