**Overcoming the Limitations of Classic Datasets with Genera

Overcoming the Limitations of Classic Datasets with Generative Adversarial Networks (GANs)

When it comes to machine learning, having a large and diverse dataset is often the holy grail. However, this is not always the case. Take, for instance, the Boston Housing dataset, a classic in the field of machine learning with a mere 506 data points. This scarcity can hinder the training and testing of machine learning models, leading to overfitting and limited generalizability.

The Limitations of Small Datasets

Small datasets can have significant consequences:

1. Overfitting: With too little data, models may overfit the training data, resulting in poor performance on unseen data.
2. Limited Generalizability: Small datasets may not capture the full range of variability in the population, leading to biased models.
3. Reduced Model Complexity: To mitigate overfitting, models may be overly simplified, sacrificing accuracy for robustness.

**Introducing Generative Adversa...

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