Limitations of Stratified Sampling

What is Stratified Sampling?

Stratified sampling is a sampling technique used in statistics and machine learning to ensure that the distribution of samples across different classes or categories remains representative of the population....

How Stratified Sampling Works?

Stratified sampling involves dividing the population into groups based on relevant characteristics, selecting samples from each group proportionately. Here’s how it works:...

Stratified Sampling in Machine Learning : Implementation

Stratified sampling ensures representative sampling of classes in a dataset, particularly in imbalanced datasets. When combined with k-fold cross-validation, it helps ensure that the performance evaluation of machine learning models remains consistent and unbiased across different folds of the dataset....

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....

Limitations of Stratified Sampling

Training and evaluating multiple times can be resource-intensive.May not fully address issues with highly imbalanced datasets.Excessive tuning based on cross-validation can lead to overfitting.Performance estimates may vary based on fold partitioning, especially with smaller datasets....

Conclusion

Stratified sampling is a key method for representative sampling, reducing bias and improving accuracy. Despite limitations, it empowers researchers to make informed decisions and drive innovation across various fields....