What are the best practices for selecting algorithms in machine learning?

What are the best practices for selecting algorithms in machine learning?

When selecting algorithms, consider the problem type, data size, and feature characteristics. Start with simpler models.

How do I know if a model is too complex for my data?

You can check for overfitting by comparing training and validation performance. If the training accuracy is high but validation accuracy is low, the model may be too complex.

Are there specific algorithms that work better for certain types of data?

Yes, for example, decision trees work well with categorical data, while linear regression is suitable for continuous data.

What role does feature engineering play in algorithm selection?

Feature engineering is crucial as it can significantly impact model performance. Good features can make simpler models outperform complex ones.