Underfitting is a common challenge in developing a machine learning (ML) model. It occurs when the model is too simplistic to capture underlying patterns or meaningful relationships in the data. As a result, the model fails to learn effectively from the training dataset and cannot generalize to new, unseen data, leading to poor predictive performance.

Underfitting is typically characterized by high bias (a consistent error across data points) and low variance (little change in predictions across different datasets) during training. These indicators generally make it easier to detect than overfitting.

Common causes of underfitting

Underfitting often stems from issues in model design, training strategy, or data quality. The following are some of the most common causes:

  • Insufficient training data or time: If the model is trained on too little data or for too few iterations, it may not learn meaningful patterns in the dataset.
  • Lack of labeled data: Labeled data — where inputs are matched with correct output values — is essential for supervised learning. Without sufficient labeled examples, the model struggles to learn accurate relationships and make reliable predictions on unseen data.
  • Overly simple model architecture: A model that is too basic may lack the capacity to capture the complexity of the task — especially in high-stakes domains like fraud detection in finance or diagnostic imaging in healthcare — leading to underfitting.
  • Suboptimal hyperparameters: Hyperparameters are configuration settings that guide the learning process (e.g., learning rate, regularization strength). Poorly tuned hyperparameters can limit the model’s ability to learn. For example, reducing overly aggressive regularization may help the model better fit the training data.

How to detect underfitting

Underfitting can often be identified by analyzing a model’s performance and learning behavior during training and validation. Key indicators include:

High bias (low model flexibility)

Bias occurs when a model fails to capture enough detail from the training data. High bias means the model is too rigid or simplistic, leading to consistently poor predictions — a hallmark of underfitting.

High training error

If the model performs poorly even on the data it was trained on, this signals underfitting. High training error suggests the model cannot effectively learn relationships within the dataset. Common evaluation metrics include accuracy, precision, and recall — depending on the specific use case (e.g., transaction classification in finance or patient outcome prediction in healthcare).

High validation error

When a model struggles to make accurate predictions on unseen (validation) data — especially when training error is also high — it often indicates underfitting. This typically suggests the model has failed to capture meaningful patterns in the data.

Learning curves

Learning curves are graphs that plot model performance (e.g., loss or accuracy) over time or training iterations. In cases of underfitting, both training and validation performance will plateau at a low level, indicating that the model is not improving despite continued training or access to more data.

Bias–variance analysis

Underfitting is associated with high bias and low variance — the opposite of overfitting, which is characterized by low bias and high variance. Simplifying a model reduces variance but increases bias, so it’s essential to balance these factors during model design and hyperparameter tuning to avoid underfitting.

Best practices for preventing underfitting 

Underfitting can be mitigated by adjusting the training process, model complexity, and data inputs to ensure the model can learn effectively. Key strategies include:

Decrease regularization

Regularization techniques are used to prevent overfitting by penalizing overly complex models. However, if the regularization penalty is too strong, it can excessively limit the model’s learning capacity. Reducing regularization allows the model to better capture complexity and variance in the data.

Increase training time or data volume

Training the model for more iterations — or on a larger, more representative dataset — gives it greater opportunity to detect meaningful patterns. This is particularly important in domains like finance and healthcare, where subtle data relationships (e.g., early indicators of risk or disease) can significantly impact outcomes.

Use transfer learning

Transfer learning involves leveraging pre-trained models to improve performance on a new but related task. This can accelerate training and enhance generalization — especially when labeled data is limited or when training from scratch would lead to underfitting. For instance, a retail demand forecasting model might benefit from features learned in a broader sales prediction context.

Enhance feature selection

Identifying and including the most relevant features (variables) in a dataset helps the model focus on informative inputs. Inadequate or overly simplistic feature sets can limit the model’s ability to detect trends. Incorporating richer, domain-specific features — such as customer segments in retail or clinical indicators in healthcare — increases model complexity and training effectiveness.

Try a different model type

The selected model may lack the necessary capacity for the problem at hand. Testing alternative model architectures or algorithms — such as switching from linear regression to a decision tree or neural network — can reveal better-suited options for learning more complex relationships.

What are some common examples of underfitting? 

Underfitting can arise across various machine learning applications when models are too simplistic or insufficiently trained to capture important patterns. Common examples include:

  • Speech recognition: Systems trained on a limited set of vocabulary, accents, or voice patterns may fail to differentiate between speakers or interpret variations in tone and frequency — resulting in inaccurate transcriptions or misclassification of spoken commands. This is especially problematic in healthcare settings, where voice-activated systems must accurately capture clinician input.
  • Image recognition: A model that cannot effectively learn hierarchical image features (such as edges, textures, and objects) may misinterpret visual inputs. This can lead to errors such as merging distinct features or failing to recognize critical elements — for example, missing subtle but important anomalies in medical imaging.
  • Predictive modeling: Models that fail to capture relevant input-output relationships produce unreliable forecasts. In finance, overlooking key indicators such as interest rate changes or market sentiment can result in misaligned investment strategies. In retail, ignoring seasonality or promotional events can lead to inaccurate demand planning and inventory mismanagement.