Error-analysis insights can motivate creating additional input features that help eliminate a particular category of errors. These new features can help with both bias and variance; if adding features increases variance in practice, regularization will usually eliminate the increase.

Google

When a learning algorithm suffers from high avoidable bias, useful techniques include increasing model size, modifying input features based on error-analysis insights, reducing or eliminating regularization, and modifying the model architecture to better suit the problem. Adding more training data is not a helpful high-bias technique because it helps variance but usually has no significant effect on bias.

Techniques for Reducing Avoidable Bias

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Modifying Input Features Based on Error Analysis to Reduce Avoidable Bias

Reducing or eliminating regularization, such as L2 regularization, L1 regularization, or dropout, can reduce avoidable bias, but it increases variance.

Learn Before

Related