Underfitting occurs when a machine learning model is too simple to capture the underlying structure of the data. In other words, the model fails to learn the patterns and relationships present in the training data, leading to poor performance not only on the training data but also on unseen data. Underfitting is essentially the opposite of overfitting.
Here are some characteristics of underfitting:
- High Bias: Underfitting is often characterized by high bias, where the model makes overly simplistic assumptions about the data. For example, a linear model might underfit a dataset that exhibits a non-linear relationship between the features and the target variable.
- Poor Performance: An underfitted model performs poorly not only on the training data but also on validation or testing data. It fails to capture the patterns present in the data and thus cannot make accurate predictions.
- Too Simple Model: Underfitting can occur when the model is too simple or lacks the capacity to represent the complexity of the data. For example, using a linear regression model to fit data that follows a non-linear pattern can result in underfitting.
Underfitting is undesirable because it means the model has not sufficiently learned from the training data, and its predictions are likely to be inaccurate. To address underfitting, one can consider:
- Increasing Model Complexity: Using more complex models, such as deep neural networks or models with more parameters, can help capture more complex relationships in the data.
- Feature Engineering: Adding more informative features or transforming existing features can provide the model with more information to learn from.
- Reducing Regularization: If regularization techniques are overly penalizing the model’s complexity, reducing their strength or removing them altogether might help.
- Adding More Data: Sometimes, underfitting occurs because the model hasn’t seen enough data to learn the underlying patterns. Adding more data to the training set can help address this issue.
Overall, the goal is to find the right balance between model complexity and the amount of available data to prevent both underfitting and overfitting, leading to a model that generalizes well to new, unseen data.
