Random Forests DATA-SCIENCE-OPTIMIZE Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   How is permutation importance computed for a feature in a random forest?

   • ABy removing the feature and retraining the model
   • BBy randomly shuffling the feature's values and measuring the drop in model performance
   • CBy calculating the feature's variance across the dataset
   • DBy counting how many trees use the feature
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2. 22application · medium

   A data scientist is training a random forest on a dataset with 10,000 samples. They want to ensure each tree is trained on a different subset of the data to introduce diversity. What is the standard method for creating these subsets?

   • AUse the entire dataset for every tree.
   • BSample with replacement to create a bootstrap sample of the same size as the original dataset.
   • CSplit the dataset into disjoint folds and train each tree on one fold.
   • DSample without replacement to create a smaller subset for each tree.
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4. 23foundation · easy

   Which hyperparameter controls the number of features considered at each split in a random forest?

   • An_estimators
   • Bmin_samples_split
   • Cmax_features
   • Dmax_depth
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5. 24foundation · easy

   How is the out-of-bag error estimated in a random forest?

   • ABy predicting each sample using only the trees for which it was out-of-bag
   • BBy cross-validating the forest on random subsets
   • CBy computing the training error on the bootstrap samples
   • DBy evaluating the forest on a held-out test set
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6. 25foundation · easy

   What is the effect of increasing the number of trees (n_estimators) in a random forest?

   • AIt always decreases training time
   • BIt generally improves performance up to a point, then plateaus
   • CIt always increases overfitting
   • DIt has no effect on model performance
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