ML0020 Data Split

A good data split in machine learning ensures that the model is trained, validated, and tested effectively to generalize well on unseen data.
The typical approach involves dividing the dataset into three sets: Training Set, Validation Set, and Test Set.

Training Set: Used to train the machine learning model. The model learns patterns and relationships in the data from this set.
Validation Set: Used to tune hyperparameters of the model and evaluate its performance during training. This helps prevent overfitting to the training data and allows you to select the best model configuration.  
Test Set: Used for a final, unbiased evaluation of the trained model’s performance on completely unseen data. This provides an estimate of how well the model will generalize to new, real-world data.

Stratification for Imbalanced Data: For imbalanced datasets, consider using stratified splits to maintain the same proportion of classes across the training and test sets.