This took longer than I expected to get right.
Start by clarifying the two modes: uniform sampling within each class (equal samples per class) and proportional sampling (N total samples with class proportions preserved). Then outline a seeded random sampling process, handling edge cases like empty classes, oversized requests, and single-class inputs by capping or redistributing samples. Finally, discuss trade-offs such as bias-variance and computational complexity.
Pro tip: Mention that you would use a fixed seed and a deterministic algorithm (e.g., random.sample with seed) to ensure reproducibility, and explicitly state how you handle edge cases like empty classes by skipping them and oversized requests by capping at available samples.
Confirm the two sampling modes: uniform within each class (equal samples per class) and proportional (N total samples with class proportions). Identify edge cases: empty classes, N larger than total samples, single-class input, and zero samples requested.
For uniform mode, determine the number of samples per class (e.g., min class size or a fixed number) and sample without replacement. For proportional mode, compute each class's share of N, then sample accordingly, handling rounding.
Use a seeded random number generator (e.g., random.seed(seed)) before sampling to ensure identical results across runs. Consider using numpy's RandomState for more control.
Skip empty classes. If N exceeds total samples, either cap at total or raise an error with a clear message. For single-class input, proportional sampling reduces to uniform sampling of that class.
Discuss time complexity O(N) and space O(N). Mention trade-offs: uniform sampling may overrepresent small classes, while proportional sampling may underrepresent them. Consider stratification benefits for imbalanced data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining stratified and flat uniform sampling in the context of imbalanced datasets, then explain how flat sampling can introduce bias by underrepresenting minority classes. Finally, discuss the benefits of stratified sampling in preserving class proportions and reducing variance in estimates, especially for A/B testing and model evaluation.
Pro tip: Mention that stratified sampling is particularly crucial for LinkedIn's A/B testing because it ensures that treatment and control groups are balanced on key dimensions, leading to more reliable and sensitive experiments.
Briefly explain what flat uniform sampling and stratified sampling are, emphasizing that flat sampling selects uniformly at random from the entire population, while stratified sampling divides the population into homogeneous subgroups (strata) and samples from each.
Describe how flat uniform sampling can introduce bias in imbalanced datasets by potentially underrepresenting minority classes, leading to biased estimates and poor model performance on those classes.
Discuss how stratified sampling ensures representation of all classes, reduces variance of estimates, and improves the accuracy of metrics like precision/recall for minority classes.
Explain that in A/B testing, stratified sampling helps balance covariates across treatment and control groups, increasing sensitivity and reducing confounding, which is vital for detecting true effects.
Conclude by stating that stratified sampling is preferable for imbalanced datasets because it mitigates bias and yields more reliable, generalizable results, especially in production ML systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.