Better ML Models with AutoML and random samples

Why Randomization is Key When Splitting Data for Machine Learning

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Essentially, Machine Learning is about learning from data. Having "good" data leads to better models, and more importantly, the quality of the information being used plays a crucial role in improving prediction accuracy.

One critical step in the process is how we separate our data into training and validation sets. If this isn’t done properly, we risk introducing bias, overfitting, or unrealistic performance expectations for the model.

In this article, we’ll explore:

• Best practices for randomization when splitting data into training and validation sets.
• Common pitfalls to avoid (such as data leakage or imbalanced splits).
• How to use a dedicated routine to ensure the process is repeatable and reliable.

The goal is to show how a robust data-splitting strategy can directly improve model performance and generalization.

What strategies do you currently use for data splitting? Do you prefer simple random splits, stratification, or more advanced approaches like time-based splits?