What is Underfitting?

When you are building a startup that relies on data, you will eventually run into the concept of model fit. Whether you are building a recommendation engine or a tool to predict customer churn, the accuracy of your logic dictates the value of your product. Underfitting is one of the primary hurdles in this process.

At its most basic level, underfitting occurs when a machine learning model is too simple. It fails to learn the relationship between your input variables and your output variables. In a scientific sense, we say the model has high bias. It has already made up its mind about what the data looks like, and it is too rigid to see the actual patterns present in the information you provide.

For a founder, this is a dangerous spot to be in. If your model is underfit, it will perform poorly while you are training it. More importantly, it will continue to perform poorly when you show it new, real world data. It essentially fails to be useful because it misses the point of the data entirely.

In the startup environment, we often prioritize speed and simplicity. We want a minimum viable product. However, if your underlying algorithm is underfit, your product does not actually function.

Imagine you are building a tool to help real estate investors predict house prices. If your model only looks at square footage and ignores location, age, or the number of bedrooms, it is likely underfitting. The model assumes a simple linear relationship between size and price that does not exist in the messy reality of the market.

Because the model is too simple, the error rate is high. You will see this high error rate during the development phase. The model cannot even get the answers right for the data it has already seen. This is the hallmark of underfitting: it is consistently wrong across the board.

There are several reasons why a model might end up underfitting your data. It usually comes down to a lack of complexity.

If you use a linear model for a problem that is non linear, you are asking for trouble. Most human behavior is complex and non linear. If you try to force that behavior into a straight line, the model will fail to capture the nuances.

Another common cause is a lack of features. Features are the individual independent variables that act as input. If you do not give the model enough information to work with, it cannot find the pattern. This is like trying to guess someone’s favorite food based only on their height. You simply do not have the right data points to make a meaningful connection.

Finally, underfitting can happen if you over-regularize your model. Regularization is a technique used to keep models simple to avoid other problems. But if you apply too much of it, you stifle the model. You prevent it from learning even the most obvious patterns.

To understand underfitting, it helps to look at its opposite: overfitting. These two concepts represent the two extremes of model error.

Overfitting is when a model is too complex. It learns the noise in your data rather than the signal. It might perform perfectly on your training data because it has essentially memorized it. But when you give it new data, it fails because it is too specific to the original set.

Underfitting is the lack of learning. The model is too simple. It fails on the training data and it fails on the new data.

Think of it like a student studying for a test. An overfit student memorizes the exact wording of the practice questions but does not understand the concepts. They fail the actual test because the questions are phrased differently. An underfit student only reads the chapter titles and does not study the details at all. They fail the practice test and the real test because they never learned the material in the first place.

As a founder, you are looking for the sweet spot in the middle. You want a model that generalizes well. This means it has learned the underlying patterns well enough to be accurate but remains flexible enough to handle new information.

One common scenario is early stage customer segmentation. You might try to group your users based on a single metric like their age. If your product is a complex B2B software, age probably has very little to do with how they use the tool. Your marketing automation will fail because your underlying model is underfit; it is too simple to account for industry, job role, or company size.

Another scenario involves financial forecasting. If you use a simple moving average to predict your future revenue without accounting for seasonality or market shifts, your forecast will be underfit. It will miss the peaks and valleys that actually define your business cycle.

In these cases, the danger is not just a technical failure. The danger is making business decisions based on a tool that provides no actual insight. You might spend capital on the wrong initiatives because your simple model suggested a trend that was not actually there.

If you identify that your model is underfitting, there are specific steps your team can take. These are not just technical tasks; they are strategic decisions about how you handle information.

First, you can increase the complexity of the model. This might mean moving from a linear regression to a neural network or a decision tree. You are essentially giving the model more ‘brain power’ to process the data.

Second, you can engage in feature engineering. This involves finding or creating new data points that might be relevant. Instead of just looking at the price of a house, you might look at the price per square foot or the distance to the nearest park. Adding these layers helps the model see the bigger picture.

Third, you can reduce the constraints on the model. If your developers are using heavy regularization, ask them to dial it back. Allow the model to explore the data more freely.

Finally, you might simply need more data. While underfitting is usually a problem of model complexity, sometimes the patterns are only visible once you have a large enough sample size.

Even with these technical solutions, we still face unknowns. One of the biggest questions is determining the exact point where a model moves from underfitting to being ‘just right.’ There is no magic number for how many features or how much data is enough.

How do we balance the cost of building a more complex model against the potential gains in accuracy? A complex model requires more computing power and more expensive talent to maintain. If a simple, slightly underfit model gets you 80 percent of the way there, is that enough for your current stage?

We also do not fully know how underfitting affects different industries differently. In healthcare, an underfit model could be life threatening. In a casual mobile game, it might just mean a slightly less optimized ad experience.

As you build your business, these are the trade offs you must navigate. Understanding that underfitting exists is the first step toward ensuring your technology actually solves the problems you set out to tackle. Avoid the trap of oversimplification. Your data has a story to tell, and you need to make sure your models are capable of listening.