Underfitting happens when models are too simplistic to capture patterns from "summary" of Machine Learning by Ethem Alpaydin
When models are too simplistic, they may fail to capture the underlying patterns in the data. This failure to capture patterns is known as underfitting. In other words, underfitting occurs when the model is not complex enough to represent the relationships between the input and output data accurately.
Simplistic models lack the flexibility and nuance required to accurately model the data. As a result, they may produce inaccurate or biased predictions. For example, a linear model may not be able to capture the non-linear relationships in the data, leading to underfitting.
Underfitting can also occur when the model is not trained on a sufficiently large dataset. A small dataset may not contain enough information for the model to learn the underlying patterns effectively. As a result, the model may generalize poorly to new, unseen data.
To address underfitting, it is essential to use more complex models that can capture the underlying patterns in the data accurately. This may involve using more sophisticated algorithms or adding additional features to the model. Additionally, increasing the size of the training dataset can help the model learn the underlying patterns more effectively.
In summary, underfitting occurs when models are too simplistic to capture the underlying patterns in the data. By using more complex models and larger datasets, it is possible to overcome underfitting and improve the accuracy of the predictions.
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