The need for diverse data in ML

As we have discussed and seen in previous chapters, diverse training data improves the generalizability of ML models to new domains and contexts. In fact, diversity helps your ML-based solution to be more accurate and better applicable to real-world scenarios. Additionally, it makes it more robust to noise and anomalies, which are usually unavoidable in practice. For more information, please refer to Diversity in Machine Learning (https://arxiv.org/abs/1807.01477) and Performance of Machine Learning Algorithms and Diversity in Data (https://doi.org/10.1051/MATECCONF%2F201821004019).

Next, let’s highlight some of the main advantages of using diverse training data in ML. In general, training and validating your ML model on diverse datasets improve the following:

• Transferability
• Problem modeling
• Security
• The process of debugging
• Robustness to anomalies
• Creativity
• Customer satisfaction

Now, let’s delve...