Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and unseen data. An underfitting model fails to learn the relationship between inputs and outputs, producing predictions that are consistently inaccurate. It's the opposite of overfitting, where a model learns the training data too well, including its noise and outliers.
Several factors can cause underfitting. Using an overly simplistic algorithm, like applying linear regression to data with complex nonlinear relationships, is a common culprit. Insufficient training time, too few features, excessive regularization, and inadequate model capacity (too few parameters or layers in a neural network) can all prevent a model from learning meaningful patterns. You can usually spot underfitting by observing high error rates on the training set itself, not just on validation data.
Fixing underfitting involves increasing the model's capacity to learn. Strategies include selecting a more complex algorithm, adding relevant features through feature engineering, reducing regularization strength, increasing training duration, and tuning hyperparameters. For neural networks, adding more layers or neurons can provide the additional capacity needed to model intricate data relationships. The goal is to find the sweet spot between underfitting and overfitting, known as the bias-variance tradeoff.
This tradeoff is a fundamental concept in machine learning: underfitting represents high bias (the model makes strong assumptions that don't match reality), while overfitting represents high variance (the model is overly sensitive to training data specifics). Practitioners typically use cross-validation, learning curves, and systematic experimentation to navigate this tradeoff and build models that generalize well.