What is the difference between feature engineering and model selection?

What is the difference between feature engineering and model selection?

Feature engineering involves creating new input features from existing data to improve model performance, while model selection is the process of choosing the best algorithm or model for a given dataset.

Can you give me examples of feature engineering techniques?

Sure! Examples include normalization, one-hot encoding, and creating interaction terms between features.

How do I choose the right model for my data?

You can start by understanding the nature of your data, the problem type (classification or regression), and then experimenting with different models using cross-validation to evaluate their performance.

Is feature engineering always necessary?

Not always, but it often helps improve model accuracy, especially with complex datasets.