Underfitting is a concept in machine learning and artificial intelligence. It happens when a model cannot capture and understand the patterns or relationships in the data it’s trained on. It usually happens because the model is too simple or lacks the right level of complexity.
To imagine underfitting, think of trying to fit a straight line to very curvy data points on a graph. If the real relationship in the data isn’t also straight, the line will miss many of the details. The result? The model does a bad job with the training data and will likely be bad on new data too.
One cause of underfitting is oversimplified algorithms. For example, a model might not have enough layers, nodes, or decision rules, depending on the type of model. Underfitting can also happen if the training time is too short, or the data sets are too small or poor quality.
Underfitting is often the opposite of overfitting. While underfitting happens in oversimplified models, overfitting happens when a model is too complex and memorizes the training data instead of generalizing it. It’s crucial to strike a balance between the two for creating reliable and useful AI models.
Example 1
Imagine you’re trying to bake cookies. A recipe simply says: “Mix ingredients, bake, then eat cookies.” Without the key details like measurements, temperature, or cooking time, your cookies will probably taste bad, if they’re even edible. This is because the recipe is too simple.
The recipe is “underfitting” the baking process – it’s too basic for such a complex process. You wouldn’t learn steps of baking from following it. Similarly, an underfit AI model doesn’t learn the important details it needs to solve a problem properly.
Example 2
Now, think about teaching a machine to tell cats from dogs in pictures. The model is trained on just a few images. In these pictures all the cats are white, and all the dogs are brown.
This oversimplified training set may make your AI model assume the only difference between cats and dogs is color. When it sees a black dog or a brown cat, it may get confused. This may lead it to make the wrong predictions because it hasn’t learned the important details, like the shape of ears or whiskers. That’s underfitting in action – it never truly picks up on the key patterns in the data.
Origin
The term “underfitting” is commonly used in machine learning, where “fitting” refers to how well a model matches or explains a dataset. It evolved alongside mathematical modeling and statistics, which predate modern AI by decades.
Originally used in statistical modeling, the concept was later applied to computational models as machine learning became more advanced. As machine learning matured, underfitting became a key topic in evaluating algorithms and building effective systems.
The basic idea of underfitting hasn’t changed much over time, but as AI has become more complex, researchers have become better at detecting it.
Additional Info
- Underfitting doesn’t just apply to machines – humans can rely on quick, shallow thinking and end up with similar results.
- To fix underfitting, it can be as simple as adding complexity and features or to train the model for longer.
- Machine learning scientists constantly juggle the risk of underfitting and overfitting when designing models.
Efficient AI requires a fine balance of simplicity and complexity. Underfitting reminds us that in both machines and life, oversimplification rarely leads to the best outcomes.