Stratify By Entity

Stratification is typically used for the target variable in supervised learning, but there are situations where stratifying by other variables is important.

These cases arise when preserving the distribution of certain features or groups in train and test sets is necessary to ensure representativeness or fairness.

Examples of Stratifying By Entities

1. Stratify by Demographic Variables

• Scenario: Predicting income levels in a dataset with demographic features like age, gender, or region.
• Reason:
  • If demographic groups are imbalanced (e.g., more data from urban regions than rural), stratifying by demographic variables ensures these groups are proportionally represented in both train and test sets.
• Example:
  • In a healthcare dataset, stratify by “gender” to ensure both male and female patients are adequately represented in the training and test sets.

2. Stratify by Geographic or Temporal Features

• Scenario: Predicting weather conditions using data from multiple locations or time periods.
• Reason:
  • Stratifying by location ensures that the model is exposed to data from a variety of regions during training and that test results are generalizable across regions.
  • Temporal stratification (e.g., by seasons) helps prevent train/test splits biased by specific time periods.
• Example:
  • Stratify by “region” in a crop yield dataset to ensure fair representation of different climates in the training and test sets.

3. Stratify by Group Membership

• Scenario: In grouped or hierarchical datasets, where multiple records correspond to the same entity (e.g., customers, patients, sensors).
• Reason:
  • Ensure proportional representation of groups in both train and test sets to avoid bias.
• Example:
  • Stratify by “customer ID” in an e-commerce dataset to ensure all customer types (e.g., high spenders vs. low spenders) are proportionally represented.

4. Stratify by Data Collection Batches

• Scenario: Predicting outcomes from experimental or survey data collected in multiple batches or timeframes.
• Reason:
  • Batch effects (e.g., differences in survey methodology or sensor calibration) can introduce biases if not accounted for.
• Example:
  • Stratify by “batch ID” to ensure the training and test sets are not dominated by specific batches.

5. Stratify by Outcome Segments in Regression Problems

• Scenario: A regression problem with continuous outcomes that have meaningful clusters or categories.
• Reason:
  • If the dataset has skewed distributions (e.g., most houses priced under $500,000 with few above $1 million), stratifying by price range (binned) ensures fair representation of all ranges.
• Example:
  • Create bins for house prices (e.g., <$500K, $500K-$1M, >$1M) and stratify by these bins.

6. Stratify by Rare Events

• Scenario: Detecting rare phenomena in datasets with extreme imbalance in a feature other than the target variable.
• Reason:
  • Ensure rare events or attributes (e.g., certain weather patterns, device types) are present in both train and test sets.
• Example:
  • Stratify by “device type” in IoT data if some devices generate far fewer observations.

What Happens If We Don’t Stratify by Non-Target Variables When Needed?

1. Bias in Train/Test Sets:

   • If certain groups (e.g., demographic groups, geographic regions) are over- or underrepresented in one subset, the model may fail to generalize to unseen data.

2. Misleading Performance Metrics:

   • Metrics may not reflect true performance, as the test set may overrepresent or underrepresent specific groups.

3. Unfairness or Ethical Issues:

   • Ignoring stratification for sensitive attributes (e.g., gender, race) may lead to biased models that disproportionately perform poorly on minority or disadvantaged groups.

4. Overfitting to Dominant Groups:

   • The model may overfit to the characteristics of dominant groups in the training set, failing to generalize to underrepresented groups in the test set.

When Stratifying by Non-Target Variables Is Not Needed

• Balanced Groups:
  • If all groups are naturally well-represented in the dataset, stratification may be unnecessary.
• Homogeneous Data:
  • If the dataset does not have significant groupings or subpopulations, random splitting suffices.