Interview Q&A / Machine Learning

1. How does a decision tree work, and why does a random forest usually perform better?

   A decision tree splits data with a series of yes/no questions, choosing each split to make the groups as pure as possible. A single deep tree overfits easily. A random forest trains many trees on random samples of rows and features and averages their votes, which reduces variance and usually improves accuracy.

   IntermediateIn-depth4 min read
2. What is bootstrap sampling and the out-of-bag score in a random forest?

   Each tree trains on a bootstrap sample: n rows drawn with replacement, containing about 63% of the unique rows. The remaining roughly 37% are out-of-bag for that tree and give a free validation estimate.

   IntermediateQuick answer1 min read
3. Which hyperparameters matter most in a random forest?

   n_estimators (more trees gives stable results with diminishing returns), max_features (how many features each split considers), max_depth, min_samples_leaf and min_samples_split. max_features and min_samples_leaf usually matter most.

   IntermediateQuick answer1 min read