Suppose you are training a machine learning model to predict house prices based on various features such as square footage, number of bedrooms, and location. After training the model, you notice that it performs poorly on both the training and validation sets. The predictions are consistently off, and the model doesn't capture the underlying patterns in the data effectively. What might be the issue, and how could you address it$?$

Group of answer choices

The model is converging too slowly. To address this, increase the learning rate during training.

The model is perfectly fitted to the data, and no further action is needed.

The model is overfitting. To address this, reduce the complexity of the model by removing some features or applying regularization.

The model is underfitting. To address this, increase the complexity of the model by adding more features.