Underfitting

Definition

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test datasets. This often happens when the model lacks sufficient complexity or when the training data is not adequately represented. Underfitting can lead to high bias, as the model fails to learn from the data and produces inaccurate predictions.

5 Must Know Facts For Your Next Test

1. Underfitting can occur in both supervised and unsupervised learning scenarios when the model's capacity is insufficient for the complexity of the data.
2. Common causes of underfitting include using too few features, selecting overly simplistic models, or not training for enough epochs in neural networks.
3. Visualizing training and validation error can help identify underfitting, where both errors remain high as model complexity increases.
4. Techniques to reduce underfitting include increasing model complexity, using more relevant features, or tuning hyperparameters.
5. A classic example of underfitting is a linear regression model trying to fit a quadratic relationship, which would result in high errors across all datasets.

Review Questions

• What are some common causes of underfitting in machine learning models?
  • Common causes of underfitting include using overly simple models that lack the necessary complexity to capture data patterns, employing too few features that miss critical information, and inadequate training duration which prevents the model from learning effectively. Additionally, overly aggressive regularization can also lead to underfitting by limiting the model's ability to fit the data properly.
• How does underfitting relate to the bias-variance tradeoff in machine learning?
  • Underfitting is closely related to bias in the bias-variance tradeoff. A model that is underfitting exhibits high bias because it makes strong assumptions about the data and fails to capture its underlying complexity. In contrast, a well-balanced model should have low bias and low variance, enabling it to generalize effectively while still fitting the training data accurately.
• Evaluate how you would approach solving an issue of underfitting in a neural network designed for image classification.
  • To address underfitting in a neural network for image classification, I would first analyze the architecture of the network to ensure it's complex enough for the task. This could involve adding more layers or neurons to increase capacity. I would also explore feature engineering strategies to include more relevant inputs, adjust hyperparameters like learning rate and epochs, and potentially reduce regularization if it’s too restrictive. Finally, validating improvements through performance metrics on both training and validation sets would confirm if underfitting has been resolved.

Related terms

Overfitting:

Overfitting happens when a model learns the training data too well, including noise and outliers, leading to poor generalization on unseen data.

Bias-Variance Tradeoff: This is the balance between two types of error: bias (error due to overly simplistic assumptions in the learning algorithm) and variance (error due to excessive sensitivity to fluctuations in the training data).

Regularization: Regularization techniques are methods used to prevent overfitting by adding a penalty for complexity to the loss function, helping to keep models simple and generalizable.