Understanding underfitting: How to avoid weak AI models

Imagine you are developing an AI model that simply does not deliver the desired results. It does not recognize patterns, makes inaccurate predictions, and generally displays poor performance. This problem is referred to as underfitting. It is the opposite of overfitting, where a model learns the training data too well and, as a result, generalizes poorly.

In this article, I will explain what underfitting is, why it occurs, and what measures you can take to prevent it.

What does Underfitting mean?

Definition

Underfitting occurs when an AI model is unable to capture the underlying patterns in the data. The model either has too low complexity or has not been trained sufficiently to interpret the data correctly.

Typical Signs of Underfitting

• Poor accuracy on the training data.

• Poor performance on both training and test data.

Comparison with Overfitting

While overfitting means that a model internalizes the training data too much and therefore generalizes poorly to new data, underfitting shows the opposite problem: the model learns too little and fails to capture the patterns in the data.

Why does Underfitting occur?

1. Too Simple Model Architecture

A model that is too simple may not able capture complex relationships in the data. An example would be a linear model trying to analyze non-linear data.

2. Insufficient Training

If a model is not trained long enough or the learning rate is too low, it may not fully recognize the underlying patterns.

3. Poor Data Quality

• Incomplete data: Missing or irrelevant information can hinder learning.

• Noise: Too many distracting data points make it hard for the model to recognize important patterns.

4. Inadequate Feature Selection

If the input data is not representative of the problem, the model will struggle to recognize meaningful patterns.

What effects does Underfitting have?

1. Low Accuracy

An underfitted model delivers poor results, as it cannot correctly analyze either the training or test data. This makes it unreliable and unsuitable for practical applications.

2. Waste of Resources

A model that does not work properly consumes unnecessary time, computing power, and financial resources without producing useful outcomes.

3. Lack of Generalization Ability

Underfitted models are unable to correctly analyze new, unknown data. This renders them useless for real-world scenarios.

Strategies to Avoid Underfitting

1. Choose a More Complex Model

If your model is too simple, you should opt for a more powerful architecture. For example, you could use a neural network with multiple layers instead of a linear model to better capture complex patterns.

2. Optimize the Hyperparameters

• Increase the learning rate: A higher learning rate can help your model recognize patterns faster and more effectively.

• Extend the training: Give your model more time to understand the data by increasing the number of training iterations.

3. Improve Data Quality

• Collect more data: A larger dataset can help the model better understand the underlying patterns.

• Clean the data: Remove irrelevant or erroneous entries that could confuse the model.

4. Utilize Feature Engineering

• Select more meaningful input variables that better represent the problem.

• Use techniques like normalization or dimensionality reduction to make the data more understandable for the model.

Examples of Underfitting in Practice

1. Image Recognition

A model that only considers basic features such as color intensity or edges will fail in complex image classification tasks.

2. Stock Price Prediction

A model that does not take into account historical trends or external factors such as market conditions will not be able to make accurate predictions.

3. Text Classification

A simple model that only analyzes individual words will struggle to understand the context and meaning of a sentence.

How to Fix Underfitting?

Case Study: Healthcare

A hospital used an AI model to predict patient risk. Initially, the model was underfitted, as it only considered basic data like age and gender. After integrating additional variables such as medical history, lab values, and medication intake, the model's accuracy improved significantly.

Tools for Detecting and Fixing Underfitting

• TensorFlow and Torch: These frameworks provide extensive capabilities to monitor and adjust models during training.

• Keras: With Keras, you can flexibly change learning rates, epoch numbers, and other parameters to avoid underfitting.

• Scikit-Learn: Ideal for simple machine learning models where you can quickly increase model complexity.

Future Perspectives: Less Underfitting through Innovations

1. Automated Feature Engineering

Future tools will be able to automatically select the best input variables, thereby reducing the likelihood of underfitting.

2. Hybrid Models

The combination of different learning methods, such as supervised and unsupervised learning, could help minimize underfitting.

3. Automatic Optimization Tools

Hyperparameter tuning tools are becoming increasingly accessible, enabling the creation of optimal models more quickly and efficiently.

Conclusion

Underfitting is a common problem that can significantly impair the performance of your AI model. With the right strategies – from choosing a suitable model to improving data quality – you can ensure that your model learns efficiently and delivers accurate results.

Stay patient, analyze the causes, and optimize your model step by step. With a thoughtful approach, you can develop a powerful model that not only works but also achieves impressive results.