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🎬Video•20 min

Data Sampling Strategies

Learn various data sampling techniques for effective model training.

Data Sampling Strategies

Why Sampling Matters

Large datasets require smart sampling for:

  • Faster experimentation
  • Balanced representation
  • Memory constraints

    • Common Techniques

    1. Random Sampling

  • Simple and unbiased
  • May miss rare classes

    • 2. Stratified Sampling

  • Maintains class proportions
  • Essential for imbalanced data

    • 3. Systematic Sampling

  • Select every nth item
  • Good for time-series

    • 4. Weighted Sampling

  • Over-sample rare classes
  • Under-sample common classes

    • Best Practices

  • Always shuffle your data
  • Use stratified splits for classification
  • Consider temporal order for time-series

    • 🎯 Key Takeaways

    • ✓Stratified sampling maintains class proportions
    • ✓Weighted sampling helps with imbalanced data
    • ✓Always shuffle data before splitting
    • ✓Consider the nature of your data when choosing

    📚 Additional Resources

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