Editing Overfitting (section)

===Resolving underfitting===
There are multiple ways to deal with underfitting:

# 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.<ref name=":0">{{Cite web |date=2017-11-23 |title=ML {{!}} Underfitting and Overfitting |url=https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/ |access-date=2023-02-27 |website=GeeksforGeeks |language=en-us}}</ref>
# 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.<ref name=":0" />
# Increase the amount of training data: If the model is underfitting due to a 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.<ref name=":0" />
# 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.<ref>{{Cite journal |last1=Nusrat |first1=Ismoilov |last2=Jang |first2=Sung-Bong |date=November 2018 |title=A Comparison of Regularization Techniques in Deep Neural Networks |journal=Symmetry |language=en |volume=10 |issue=11 |pages=648 |doi=10.3390/sym10110648 |bibcode=2018Symm...10..648N |issn=2073-8994 |doi-access=free }}</ref>
# [[Ensemble Methods]]: Ensemble methods combine multiple models to create a more accurate prediction. This can help reduce underfitting by allowing multiple models to work together to capture the underlying patterns in the data.
# [[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 improve the accuracy of the model and prevent underfitting.<ref name=":0" />