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

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