Glossary
Underfitting
Underfitting is a concept in machine learning that occurs when a model fails to capture the underlying patterns in the data. It usually happens when the model is too simple or lacks the necessary complexity to accurately represent the relationship between the input variables and the target variable.
When a model underfits the data, it means that it is unable to capture the nuances and complexities present in the dataset. As a result, the model's predictions are not accurate and may perform poorly on both the training and testing datasets.
Underfitting can be identified by observing the model's performance metrics, such as accuracy or error rate. If the model consistently performs poorly and fails to make accurate predictions, it may be a sign of underfitting.
There are several factors that can lead to underfitting. One common cause is using a model that is too simple for the complexity of the data. For example, using a linear regression model to predict a non-linear relationship between the variables can lead to underfitting.
Another factor is the lack of relevant features or insufficient data. If the model does not have access to all the necessary information or if the dataset is limited, it may not be able to accurately capture the underlying patterns.
To address underfitting, several techniques can be employed. One approach is to increase the complexity of the model by using more advanced algorithms or increasing the number of features. This allows the model to better capture the underlying patterns in the data.
Regularization techniques can also be used to prevent overfitting and underfitting. Regularization adds a penalty term to the model's objective function, which discourages overly complex models. This helps strike a balance between model complexity and generalization.
In conclusion, underfitting is a phenomenon in machine learning where a model fails to capture the underlying patterns in the data. It can be caused by using a model that is too simple or lacks the necessary complexity. By employing appropriate techniques, such as using more advanced algorithms or regularization, underfitting can be mitigated, leading to improved model performance.
A wide array of use-cases
Discover how we can help your data into your most valuable asset.
We help businesses boost revenue, save time, and make smarter decisions with Data and AI