1. What is underfitting?
Underfitting refers to a situation where a model fails to capture the underlying patterns or relationships in the data due to its simplicity or lack of complexity.

2. What causes underfitting?
Underfitting can occur when a model is too simple, lacks relevant features, or when there is insufficient training data.

3. What are the characteristics of underfitting?
Underfitting often leads to high bias and low variance in the model. It oversimplifies the relationships in the data, resulting in poor predictive performance.

4. What are the signs of underfitting?
Signs of underfitting include low performance on the training data and an inability to improve with more training iterations or model complexity.

5. What are the effects of underfitting?
Underfitting can result in poor predictive accuracy and generalization. The model may fail to capture the complexities of the data, leading to suboptimal performance on new, unseen data.

6. How can underfitting be addressed?
Underfitting can be addressed by increasing model complexity, adding more features or parameters, using advanced model algorithms, or collecting more diverse and representative training data. Regularization techniques can also help prevent overfitting and improve model performance.

7. How do you balance overfitting and underfitting?
Finding the right balance between overfitting and underfitting is crucial. While underfitting can be mitigated by increasing model complexity, it's important to avoid overfitting, where the model becomes too specific to the training data and fails to generalize well to new data.