Why is data quality important in machine learning, and how can poor data quality affect model performance?

Data quality is crucial in machine learning because the performance of a model is heavily dependent on the quality of the input data. Poor data quality—such as inaccuracies, missing values, or inconsistencies—can lead to unreliable predictions and poor generalization to unseen data. For instance, training a model on biased or noisy data can cause it to learn incorrect patterns, ultimately resulting in misleading conclusions. Furthermore, ensuring high data quality can enhance model robustness and improve interpretability, which are vital for deploying models in real-world applications.