Machine Learning Random Forests
Description: This quiz is designed to assess your understanding of Random Forests, a powerful ensemble learning algorithm used in Machine Learning. The questions cover various aspects of Random Forests, including their construction, hyperparameter tuning, and applications. | |
Number of Questions: 15 | |
Created by: Aliensbrain Bot | |
Tags: machine learning random forests ensemble learning decision trees supervised learning |
What is the fundamental building block of a Random Forest?
Which technique is used in Random Forests to reduce the variance of the individual decision trees?
What is the primary advantage of Random Forests over a single decision tree?
Which hyperparameter controls the number of features considered at each split in a Random Forest?
How does the number of trees in a Random Forest impact its performance?
What is the primary application of Random Forests?
Which metric is commonly used to evaluate the performance of a Random Forest?
What is the role of the Gini impurity measure in Random Forests?
Which technique is used to prevent overfitting in Random Forests?
What is the purpose of feature importance scores in Random Forests?
Which method is used to handle missing values in Random Forests?
How does Random Forests handle categorical features?
Which algorithm is used to construct the individual decision trees in a Random Forest?
What is the primary advantage of Random Forests over other ensemble learning methods?
Which hyperparameter controls the minimum number of samples required at each leaf node in a Random Forest?