What are the common mistakes to avoid when choosing a model?

What are the common mistakes to avoid when choosing a model?

Some common mistakes include not understanding the problem, ignoring data quality, and overfitting.

Can you explain how data quality affects model selection?

Absolutely! Poor data quality can lead to inaccurate predictions and misguides the model training process.

What is overfitting and how can I avoid it?

Overfitting occurs when a model learns noise instead of the signal. You can avoid it by using techniques like cross-validation.

Are there specific metrics I should consider when evaluating models?

Yes, metrics like accuracy, precision, recall, and F1 score are crucial depending on your specific use case.