Resolving underfitting
There are multiple ways to deal with underfitting:

1) Increase the complexity of the model: If the model is too simple, it may be necessary to increase its complexity by adding more features, increasing the number of parameters, or using a more flexible model. However, this should be done carefully to avoid overfitting.
2) Use a different algorithm: If the current algorithm is not able to capture the patterns in the data, it may be necessary to try a different one. For example, a neural network may be more effective than a linear regression model for some types of data.
3) Increase the amount of training data: If the model is underfitting due to lack of data, increasing the amount of training data may help. This will allow the model to better capture the underlying patterns in the data.
4) Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages large parameter values. It can also be used to prevent underfitting by controlling the complexity of the model.
5) Ensemble Methods: Ensemble methods combine multiple models to create a more accurate prediction. This can help to reduce underfitting by allowing multiple models to work together to capture the underlying patterns in the data.
6) Feature engineering: Feature engineering involves creating new model features from the existing ones that may be more relevant to the problem at hand. This can help to improve the accuracy of the model and prevent underfitting.
Extract the different ways of dealing with underfitting mentioned in the text
The different ways of deal with underfitting mentioned in the text are:
- Increase the complexity of the model
- Use a different algorithm
- Increase the amount of data
- Regularization
- Ensemble Methods
- Feature engineering