Bias-Variance Tradeoff
The balance between a model’s complexity (variance) and its assumptions about the data it’s learning from (bias).
Areas of application
- Machine learning
- Data science
- Statistics
- Pattern recognition
- Deep learning
Example
For instance, a neural network with too much variance might overfit a training dataset, resulting in poor performance on new, unseen data. On the other hand, a model with too much bias might underfit the training data, failing to capture the underlying patterns and relationships.