What Underfitting Meaning, Applications & Example
When a model fails to capture the underlying patterns in the data.
What is Underfitting?
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. As a result, the model performs poorly on both the training data and the test data. Underfitting typically happens when the model has insufficient complexity or when it is not trained long enough to learn the important features of the data.
Causes of Underfitting
- Too Simple Model: A model that is too simplistic (e.g., a linear model for a complex problem) cannot capture the complexity of the data.
- Insufficient Features: If the model lacks important features or variables, it may fail to account for critical aspects of the data.
- Excessive Regularization: Overuse of regularization techniques, such as L1 or L2, can make the model too constrained, preventing it from learning properly.
Applications of Underfitting
- Regression Problems: A linear regression model trying to predict a nonlinear relationship might underfit, leading to poor predictions.
- Classification Tasks: A model with too few parameters or overly restrictive decision boundaries might fail to classify data accurately.
- Time-Series Forecasting: Using a simple model like a moving average for complex time-series data can result in underfitting, as it fails to capture important temporal patterns.
Example of Underfitting
In house price prediction, if a simple linear regression model is used to predict house prices based only on the number of bedrooms, it may underfit. It will likely miss other important factors like location, square footage, or market trends, resulting in poor performance and inaccurate price predictions.