Benefits of Stratified Sampling in Machine Learning
- Reduces Bias: By guaranteeing each important subgroup is represented, stratified sampling reduces bias in the training data. This is crucial for machine learning models to learn accurate patterns from the data.
- Improves Model Performance on Imbalanced Datasets: In cases where some classes are rare (like spam emails), stratified sampling ensures the model has enough examples to learn from those classes effectively.
Stratified Sampling in Machine Learning
Machine learning can be a challenge when data isn’t balanced. Stratified sampling is a technique that ensures all the important groups within your data are fairly represented. In this tutorial, we will understand what is stratified sampling and how it is crucial that it leads to superior machine learning models.