Random Forests DATA-SCIENCE-OPTIMIZE Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A team is tuning a random forest for a binary classification task. The model currently has 100 trees, unlimited depth, and uses the default number of features per split. The validation accuracy is high, but the training accuracy is near perfect, indicating overfitting. Which hyperparameter change is most likely to reduce overfitting?

   • AIncrease the number of trees to 500.
   • BRemove the random feature subsampling.
   • CIncrease the maximum depth of each tree.
   • DLimit the maximum depth of each tree.
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2. 7application · medium

   A data scientist is using a random forest for a regression task and wants to understand which features are most influential. They notice that impurity-based importance is giving high scores to a categorical feature with many categories. What is the most likely reason for this bias?

   • AImpurity-based importance is not affected by cardinality.
   • BImpurity-based importance tends to favor features with many unique values because they can create more splits that reduce impurity.
   • CThe feature is actually the most important, so the score is correct.
   • DImpurity-based importance is biased toward features with low cardinality.
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4. 8foundation · easy

   Why does a random forest randomly select a subset of features at each split?

   • ATo increase the depth of each tree
   • BTo reduce the number of trees needed
   • CTo decorrelate the trees in the forest
   • DTo speed up training by using fewer data points
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5. 9foundation · easy

   Which scenario is a suitable use case for a random forest model?

   • APredicting customer churn from a mix of numerical and categorical features
   • BDetecting anomalies in high-dimensional time series with strict latency requirements
   • CGenerating synthetic images for a generative model
   • DModeling a linear relationship between two variables
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6. 10foundation · easy

   What are out-of-bag (OOB) samples in a random forest?

   • AData points with missing values
   • BData points used for final model testing
   • CData points that are misclassified by the forest
   • DData points not included in a tree's bootstrap sample
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