High accuracy on training data is often a lie. To find the truth, you must hide some data from your model and see how it handles the unknown.
1The Honest Split
The Train/Test Split is the first step in any ML pipeline. By training on one subset and testing on another, we simulate real-world conditions where the model encounters unseen data. This is the only way to detect Overfitting, where a model 'memorizes' the training noise.
2Cross-Validation Logic
Sometimes a single split is unrepresentative. K-Fold Cross Validation solves this by dividing the data into 'K' sections. The model runs 'K' times, each time using a different section for testing. The final score is the average of all runs, providing a much more stable metric.
3The Random State
Reproducibility is key in science. By setting a random_state, you ensure that every time you run your split, you get the exact same results. This allows other researchers to verify your findings and ensures your development environment remains consistent.
