Machine Learning Basics
April 7, 2025

What is Underfitting ?

Contributors
Dhanashree B
Product Marketing Manager
Updated on
April 7, 2025

What is Underfitting?

Underfitting happens when a machine learning model is too simple and fails to detect the main patterns in the data. This occurs when the model lacks enough complexity or the training data does not well represent the overall population. Underfitting results in bad performance on both the training and testing datasets. According to Ng (2015), underfitting can be spotted when both training and validation errors are high and alike.

Why is Underfitting Important?

Performance Optimization: Knowing and tackling underfitting is key for boosting the performance of machine learning models. It ensures the model learns needed patterns without being too simple.

Model Selection: This stresses the necessity to pick a suitable model and tune its settings to match the complexity of the data well.

Data Utilization: Handling underfitting makes sure that the available data is used well improving the model's ability to predict.

What are the Solutions to Underfitting?

  • Increase Model Complexity: Use a harder model or include additional features to understand the underlying patterns in the data.
  • Longer Training Time: Let the model train for more time or use smarter training algorithms.
  • Feature Engineering: Make new features from the current ones to give the model more information.
  • Reduce Regularization: If regularization techniques such as L1 or L2 are too tough, they can be eased to help the model fit the data well.

How Alltius AI Enables Organizations to reduce underfitting?

Alltius' provides leading enterprise AI technology for enterprises and governments to harness and extract value from their current data using variety of technologies, including removing underfitting. Alltius' Gen AI platform enables companies to create, train, deploy and maintain AI assistants for sales, support agents and customers in a matter of a day. Alltius platform is based on 20+ years of experience at leading researchers at Wharton, Carnegie Mellon and University of California and excels in improving customer experience at scale using Gen AI assistants catered to customer's needs. Alltius' successful projects included but are not limited to Insurance(Assurance IQ), SaaS (Matchbook), Banks, Digital Lenders, Financial Services (AngelOne) and Industrial sector(Tacit).

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