The bias-variance tradeoff is the property where a predictive model with a low bias has a higher variance and vice versa.

• High bias can cause a model to miss important relations between features and target outputs.
• High variance can lead to the random noise of the training set to be modeled, namely overfitting. This, in turn, will likely lead to greater errors when introduced to the testing set.