Explore key techniques for tuning random forests and interpreting feature importance with this quiz. Assess your grasp of hyperparameter settings, feature selection strategies, and common best practices in advanced random forest modeling.
Which parameter should be increased to reduce variance and optimize performance when your random forest model is underfitting?
Explanation: Increasing the number of trees generally helps in reducing variance and improving the robustness of a random forest. Max depth, if set higher, can lead to overfitting rather than reducing variance. Minimum samples per leaf controls overfitting more than variance reduction. Feature scaling is typically unnecessary for random forests and does not impact tree count.
What aspect of random forests ensures that individual trees are diverse and uncorrelated?
Explanation: Bootstrap sampling creates different datasets for each tree, enhancing model diversity. Learning rate is related to boosting algorithms, not random forests. Early stopping is not a standard feature in random forests. K-means clustering relates to unsupervised learning, not to the randomness of tree building.
Which method is commonly used in random forests to estimate feature importance by evaluating the decrease in impurity?
Explanation: Gini importance measures the reduction in impurity attributed to each feature within the random forest. Principal component analysis is used for dimensionality reduction, not for importance computation. Hyperparameter tuning optimizes model parameters but does not calculate importance. One-hot encoding transforms categorical data but does not assess feature relevance.
If you decrease the 'max features' parameter when fitting a random forest, what outcome is most likely?
Explanation: Reducing 'max features' increases randomness by allowing each tree to consider fewer features, which lowers inter-tree correlation. The number of trees remains set by another parameter. Feature scaling generally does not affect random forests. Training loss convergence rate is not directly controlled by max features—it's more influenced by stability and diversity.
When interpreting permutation feature importance in random forests, what does a high drop in model accuracy indicate for a feature?
Explanation: A large decrease in accuracy when a feature is randomized shows it is important to model performance. Redundant features or those with constant values won’t affect accuracy when permuted. Feature importance does not directly depend on whether a feature is categorical; it's about its predictive value.
What is the main advantage of using the Out-Of-Bag (OOB) score when evaluating random forests?
Explanation: The OOB score offers a built-in validation metric by using data not seen by each tree, making cross-validation unnecessary in many cases. It does not speed up training nor increase features per split. Hyperparameter tuning is a separate step, and OOB does not eliminate its need.
Which parameter is most directly responsible for controlling overfitting in a random forest?
Explanation: Maximum tree depth restricts how complex individual trees can become, helping prevent overfitting. Number of estimators controls model variance, not overfitting itself. Bootstrap ratio affects dataset sampling, not tree complexity. Standardization is not necessary for random forests and doesn’t control overfitting.
If a random forest assigns extremely high importance to a feature that should have limited predictive power, what is a likely explanation?
Explanation: Feature leakage occurs when a variable reveals information about the target inappropriately, leading to inflated importance scores. Hyperparameter bias does not directly cause one feature to dominate. Slow convergence is not related to feature importance values. Feature scaling errors aren't relevant as random forests are insensitive to scaling.
In a random forest, if two features are highly correlated, how might their importances be affected?
Explanation: When two features are highly correlated, the model often distributes importance between them, making each seem less individually important. It is not guaranteed they’ll always be ranked highest or ignored. Standardizing values is not required for importance calculation in random forests.
What is a good strategy if your random forest model shows several features with near-zero importance in multiple runs?
Explanation: Removing consistently unimportant features helps simplify the model and can improve training speed. Increasing max depth or decreasing the number of trees isn't directly related to feature irrelevance. Feature scaling typically isn’t necessary, as random forests handle different feature scales naturally.