While model training offers numerous benefits, it also comes with challenges:
Data Quality: Poor data quality can lead to inaccurate models. Ensuring clean, relevant, and unbiased data is crucial. Overfitting: Overfitting occurs when a model performs well on training data but poorly on unseen data. Techniques like cross-validation can help mitigate this. Feature Selection: Choosing the right features is essential for model accuracy. Irrelevant or redundant features can negatively impact performance. Computational Resources: Training complex models, especially neural networks, can require significant computational power and time.