Supportsoft Glossary

Preventing Overfitting in Machine Learning Models

 When a machine learning algorithm becomes overfitted, it has learned the training examples too precisely, including those irregularities and patterns that cannot be extrapolated to novel examples. Although it may perform better during tests on the training examples, when subjected to new conditions, it usually produces weak results.

Using machine learning for businesses can result in making inaccurate predictions and causing companies to make poor decisions if the application of the model is misused. Typically, models will perform as predicted during the testing process but will likely fail to produce accurate predictions for changing variables or previously unseen examples.

There are a variety of strategies that can be applied during the training of machine learning algorithms to mitigate their ability to become overfitted, including using cross-validation, regularisation, data augmentation, and early stopping. Additionally, using diverse and representative training datasets can also help reduce overfitting.

For any organisation that provides information technology or IT-enabled services, effective management of overfitting is critical to deploying adaptive, responsive, and scalable artificial intelligence solutions. Well-generalised machine learning models will demonstrate predictable performance across different platforms and environments.

By taking proactive measures to address overfitting at the earliest phases of development, organisations will improve the reliability of their machine learning models while simultaneously reducing the potential for harm. Moreover, they will create a basis for deriving value from alignment with their organisational objectives.