How This Workflow Works
This workflow applies several sampling techniques to a large dataset to create smaller, representative subsets. It demonstrates random, linear, stratified, and equal size sampling methods, each designed to address different analytical needs.
Key Features:
- Create manageable data samples for faster analysis and testing
- Ensure proportional representation of groups or categories in samples
- Generate samples with equal group sizes for balanced comparisons
- Compare the effects of different sampling strategies
Step-by-step:
1. Apply Random and Linear Sampling:
Random sampling selects rows unpredictably, giving every record an equal chance of being chosen. Linear sampling, on the other hand, takes the first set number of rows, which is useful when order matters or for quick initial checks.
2. Use Stratified Sampling for Group Representation:
Stratified sampling divides the data into groups (such as categories or classes) and then samples from each group in proportion to its size. This ensures that the sample reflects the original distribution of groups, which is important for fair analysis.
3. Create Equal Size Samples for Balanced Analysis:
Equal size sampling selects the same number of rows from each group, either exactly or approximately. This approach is useful when you want to compare groups directly without bias from unequal group sizes.