To reduce avoidable bias, options include training a larger model, training longer or using improved optimization algorithms, and searching for a better neural network architecture or hyperparameters. To reduce variance, options include collecting more training data, adding regularization (e.g., L2 regularization, dropout, or data augmentation), and also searching for a better neural network architecture or hyperparameters.

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University of Michigan - Ann Arbor

Avoidable bias is the difference between a model's training error rate and the optimal error rate (Bayes error rate). In practice, since the Bayes error rate is difficult to calculate, human-level error is often used as a proxy. Avoidable bias is estimated as:
$$\text{Avoidable Bias} = \text{Training Error} - \text{Human-level Error}$$
A high avoidable bias indicates that the model is underfitting the training data.

Avoidable bias

- Train a bigger neural model.
- Train with better optimization algorithms.
- Find better neural network architecture.

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