Train-Test Split Simulator

Train-test split divides a dataset into two subsets:

• Training Set: Used to train/fit the model (typically 70-80%)
• Test Set: Used to evaluate model performance (typically 20-30%)

This prevents overfitting by evaluating on unseen data.

• Random Split:
  Randomly shuffle and divide data
  ✓ Simple and unbiased
  ✗ May create imbalanced class distributions
• Stratified Split:
  Maintains class proportions in both sets
  ✓ Ensures representative splits for classification
  ✓ Recommended for imbalanced datasets
  Requires: Label/target column
• Time-Based Split:
  First N% for training, last M% for testing
  ✓ Respects temporal ordering
  ✓ Prevents data leakage in time series
  Use for: Time series, sequential data
• Group-Based Split:
  Keep groups together (don't split within groups)
  ✓ Prevents data leakage from related samples
  Use for: Patient studies, user data, hierarchical data

• Test Size: Typically 20-30% of dataset
  • Smaller datasets: 20-25% test
  • Larger datasets: Can use smaller % (e.g., 10%)
• Random Seed: Set for reproducibility
• Stratification: Use for classification with imbalanced classes
• Time Series: Always use time-based split, never random
• Validation Set: Consider train-validation-test (e.g., 60-20-20)

• Data Leakage:
  Information from test set influences training
  Solutions: Time-based split for temporal data, group split for related samples
• Class Imbalance:
  Random split may create very imbalanced test set
  Solution: Use stratified split
• Small Datasets:
  Too little data for reliable test set
  Solution: Use cross-validation instead
• Temporal Ordering:
  Random split breaks time dependencies
  Solution: Use time-based split

• Model Evaluation: Assess performance on unseen data
• Hyperparameter Tuning: Train on train set, validate on test set
• Feature Selection: Select features using training data only
• Comparison: Compare different models fairly
• Deployment Planning: Estimate real-world performance