What is underfitting?

What is underfitting?

• A. When a model is too simple to capture patterns
• B. When a model is overtrained on data
• C. When a model has perfect fit
• D. When a model memorizes training data

Answer: (A)

Underfitting happens when the model is too simple to capture patterns in the data. Because of its limited capacity, it can’t fit the training data well, leading to high error on both the training data and new data. This often comes from using a model with too little complexity, too few features, or overly strong regularization. To fix it, increase the model’s capacity, add informative features, or reduce regularization so the model can learn the underlying relationships. The other scenarios describe models that fit training data too closely or memorize noise, which is overfitting or an overly perfect fit, not underfitting.

Underfitting happens when the model is too simple to capture patterns in the data. Because of its limited capacity, it can’t fit the training data well, leading to high error on both the training data and new data. This often comes from using a model with too little complexity, too few features, or overly strong regularization. To fix it, increase the model’s capacity, add informative features, or reduce regularization so the model can learn the underlying relationships. The other scenarios describe models that fit training data too closely or memorize noise, which is overfitting or an overly perfect fit, not underfitting.