Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This results in poor performance on both the training data and unseen data (test data).
Key Points:
- Low Complexity: Underfitting typically happens with models that have too few parameters or are overly simplistic relative to the complexity of the data.
- Poor Performance: The model fails to learn the relevant features, leading to low accuracy on both training and test datasets.
- Symptoms: You may observe similar low accuracy for both training and test sets, indicating that the model is not learning effectively.
Causes of Underfitting:
- Insufficient Model Complexity: Using a linear model for a nonlinear relationship.
- Inadequate Training: Not training the model for enough epochs or iterations.
- Feature Selection: Missing important features that could help the model learn better.
Solutions:
- Increase Model Complexity: Use more complex models or algorithms that can capture the underlying patterns.
- Feature Engineering: Add relevant features or use polynomial features to better represent the data.
- Longer Training: Allow the model to train for more epochs or iterations to improve learning.
By addressing underfitting, you can improve the model's ability to learn from the data and enhance its performance.
