What are some common errors in data science and how can I fix them?

What are some common errors in data science and how can I fix them?

Common errors include data leakage, overfitting, and not validating models. Would you like to know how to fix any specific one?

Yes, tell me more about overfitting.

Overfitting occurs when a model learns the noise in the training data instead of the actual patterns. You can fix it by using techniques like cross-validation, pruning, or regularization.

What is regularization?

Regularization is a technique used to prevent overfitting by adding a penalty for larger coefficients in the model. It helps to simplify the model.

Can you give an example of how to implement regularization?

Sure! In Python, you can use libraries like scikit-learn to implement Lasso or Ridge regression, which are forms of regularization.