Bias-Variance Tradeoff

Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).

Simpler models are stable (low variance) but they donâ€™t get close to the truth (high bias).

More complex models are more prone to overfitting (high variance) but they are expressive enough to get close to the truth (low bias).

The best model for a given problem usually lies somewhere in the middle.

Bias leads to a phenomenon called underfitting. This is caused by the introduction of error due to the oversimplification of the model. On the contrary, variance occurs due to complexity in the machine learning algorithm. In variance, the model also learns noise and other distortions that affect the overall performance of it. If you increase the complexity of your model, then the error will go down due to reduction in bias. However, after a certain point, the error will increase due to increasing complexity and addition of noise. This is known as bias-variance tradeoff. A good machine learning algorithm should possess low bias and low variance.