Use a more complex model. B. Changing from a linear model to a nonlinear model or adding hidden layers to the neural network often helps solve underfitting. The algorithm we use includes a default regularization parameter designed to prevent overfitting. For beginners, overfitting in data science means that the learning model relies heavily on the training data, and underfitting means that the model has a poor relationship with the training data. Ideally, both should be absent from the model, but it is usually difficult to eliminate them.

BY Best Interview Question ON 30 Dec 2023