MACHINE LEARNING

Detailed explanation-1: -As model complexity increases, performance on the data used to build the model (training data) improves. However, performance on an independent set (validation data) improves up to a point, then starts to get worse. This is called overfitting.

Detailed explanation-2: -As the model complexity increases (x-direction), the training error decreases, and the test error increases. When the model is very complex, the gap between training and generalization/test error is very high. This is the state of overfitting.

Detailed explanation-3: -As the complexity of the model rises, the variance will increase and bias will decrease. In a simple model, there tends to be a higher level of bias and less variance. To build an accurate model, a data scientist must find the balance between bias and variance so that the model minimizes total error.