What are Underfitting and Overfitting & Why It is Important

Introduction

Underfitting and Overfitting are two common challenges encountered in machine learning and statistical modelling, They both relate to how well a model generalizes to data beyond the training dataset. Understanding these concepts is crucial for building accurate and robust predictive models.

Underfitting

Underfitting happens when a model is too simplistic and fails to capture the patterns and relationships in the data. This leads to poor performance on both the training and new data. The provided diagram below depicts an example of a linear regression model that couldn’t accurately predict the target variable. In the diagram, the line appears to be too straight compared to the actual pattern of the data. Therefore, resulting in bad accuracy in training and testing.

Underfitting & Overfitting

Overfitting

On the other hand, overfitting happens when the model becomes too complex. It tries to fit the training data perfectly by memorizing the noise and outliers in the dataset. As a result, the model fails to generalize well to new, unseen data.

The diagram below illustrates an example of overfitting in a classification problem. The orange line represents the decision boundary learned by an overfitting model. It closely follows the training data points, or we say the model ‘memorises’ the data instead of learning. This makes the model biased to the training dataset and will not perform well with other datasets. Therefore, leads to poor performance when predicting the class labels of new data points.

Overfitting

Balancing Between Underfitting and Overfitting – The Perfect Fit

The aim is to find the “sweet spot” between overfitting and underfitting, where the model generalizes well to unseen data while capturing the important patterns in the training data.

Perfect Fit

Finding the right balance between underfitting and overfitting is essential for building a reliable machine-learning model. This balance can be achieved by adjusting the complexity of the model or by using regularization techniques.

Regularization methods, such as L1 or L2 regularization, penalize complex models and encourage simpler ones. These techniques help prevent overfitting by reducing the model’s ability to memorize noise in the training data.

Additionally, evaluating the model’s performance on a separate validation dataset can give insights into its generalization capabilities. Monitoring metrics such as accuracy, precision, recall, or mean squared error can help determine whether the model is underfitting, overfitting, or performing optimally.

Ways to Avoid Underfitting and Overfitting

To avoid underfitting and overfitting, there are several strategies that you can employ:

  1. Increasing Model Complexity: If your model is underfitting and not capturing the underlying patterns in the data, you may need to increase its complexity. This can be done by adding more layers, nodes, or features to your model. However, it is important to strike a balance and avoid going to the extreme of overfitting.
  2. Feature Engineering: Sometimes, the problem may not be with the model itself but with the features used to train it. Feature engineering involves creating new features or transforming existing ones to better capture the underlying relationships in the data. It can help improve the performance of both underfitting and overfitting models.
  3. Collecting More Data: Insufficient training data can contribute to both underfitting and overfitting. By collecting more data, you can provide your model with a larger and more representative sample to learn from. This can help improve its generalization performance.
  4. Cross-Validation: Cross-validation is a technique that involves splitting the data into multiple subsets for training and validation. It helps assess the model’s performance on unseen data and provides a more accurate estimate of its generalization capabilities. By evaluating the model on multiple folds of the data, you can get a better understanding of whether it is underfitting or overfitting.
  5. Early Stopping: Another technique to combat overfitting is early stopping. This involves monitoring the model’s performance on a validation set during the training process and stopping the training when the model starts to overfit. By keeping track of the validation loss or error, you can determine the optimal number of training iterations to avoid overfitting.
  6. Ensemble Methods: Ensemble methods combine the predictions of multiple models to improve overall performance. By training several models with different initialization or algorithm variations, you can reduce the risk of both underfitting and overfitting. Popular ensemble techniques include bagging, boosting, and stacking.

Remember, finding the right balance between underfitting and overfitting is a key aspect of building a successful machine-learning model. It requires careful consideration of the model’s complexity, feature engineering, data availability, and appropriate evaluation techniques.

Conclusion

In summary, underfitting and overfitting are two challenges that can hinder the performance of machine learning models. Underfitting occurs when the model is too simple and fails to capture the underlying patterns in the data, while overfitting happens when the model becomes too complex and memorizes noise and outliers in the training data. Finding the right balance and using regularization techniques can help improve a model’s performance and generalization abilities.