What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity

The consequence of a model having low bias and high variance is Overfitting.

Here's why:

1. Bias refers to the error due to the model's simplistic assumptions in learning from the data. Low bias means the model learns too well from the data, capturing the complex patterns within it.

2. Variance refers to the error due to the model's sensitivity to fluctuations in the training data. High variance means the model is too sensitive to the training data and captures the noise along with the underlying pattern.

3. When a model has low bias and high variance, it means the model is too complex and fits the training data too well, even capturing the noise in the data. This leads to overfitting, where the model performs well on the training data but poorly on unseen data (like test data), as it fails to generalize from the training data to unseen data.