Answer: Bias is the error introduced by approximating a real-world problem, variance is the model’s sensitivity to small fluctuations in the training data, and residuals are the differences between predicted and actual values.
Differences between Bias, Variance and Residuals in Tabular form :
| Aspect | Bias | Variance | Residuals |
|---|---|---|---|
| Definition | Error due to simplified assumptions and model assumptions. | Error due to model’s sensitivity to fluctuations in training data. | Differences between predicted and actual values. |
| Impact | High bias can lead to underfitting and poor model performance. | High variance can lead to overfitting and poor generalization. | Represent the unexplained variation in the data by the model. |
| Source | Inherent in the model structure and assumptions. | Arises from complex models that capture noise in the training data. | Result of the model’s inability to capture all patterns in the data. |
Conclusion:
Balancing bias and variance is crucial for optimal model performance; high bias may result in underfitting, while high variance may lead to overfitting, and understanding and minimizing residuals are essential for accurate predictions.