Striking a Balance: Overfitting vs Underfitting in ML

Striking a Balance: Overfitting vs Underfitting in ML

Machine learning (ML) models have changed the way we make business intelligence decisions. However, these powerful tools are not so perfect. They can be vulnerable to overfitting vs underfitting. As a business owner, you naturally want to get the most out of your ML investment. Therefore, if you understand these two concepts, you’ll be able to achieve the main goal — balance. Today, we’ll explore the nuances of overfit vs underfit models in more detail and reveal strategies for building robust algorithms.

What is Overfitting in ML?

It’s one of the biggest obstacles that can keep your AI programs from progressing. This hurdle is the result of your algorithms placing too much weight on the details of their test data — they simply don’t see the bigger picture behind these fragments. Many would think, what is wrong with this? It seems that it is the only way we’ll achieve very high accuracy. However, everything is not so simple. Usually, it becomes an obstacle that prevents models from running an effective generalization. The limitation becomes most apparent when the model encounters new situations. Algorithms are simply unable to evaluate new information because they have gotten stuck on evaluating the old.

What causes such behavior? Let’s look at the main factors:

  • Insufficient regularization.
  • Limited training data.
  • Complexity.

Ample study time is also important. Models with many tightly coupled neural layers, such as deep neural networks, may be susceptible to these problems.

To better understand the concept, let’s take a spam filter as an overfitting example. So, it focuses too much on the word “free” or unusual sender addresses. After all, its main goal is to protect you from spam. However, here comes the negative side of the process. The method now distinguishes legitimate emails from words like spam. As a result, it might lead to disappointed customers. The example illustrates how such technologies can often get lost behind small details and close our eyes to the big picture.


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What is Underfitting in ML?

A lack of seeing details is also a common problem. It usually happens when simplicity hinders the machine learning algorithm’s ability to grasp the subtleties of the training data. For developers, this means their models can’t learn optimally from training data. Consequently, we get a drop in productivity. Simply put, an underfit data model produces inaccurate projections, especially when unforeseen examples are encountered.

The root cause often lies in oversimplified models with assumptions that are too basic to grasp the intricacy of the evaluation data.

Imagine a weather forecast model that relies solely on temperature to forecast precipitation. Without taking into account important factors such as humidity, wind speed, and atmospheric pressure, rain may incorrectly be predicted based solely on temperature drop. At the same time, it’ll neglect the influence of other vital variables.

Indicators of Overfitting and Underfitting

So, how do we achieve that dream balance and evaluate how well our models work? Bias and variance are the main terms that come into play at this stage. Let’s get to know each of them in more detail:

  • Bias is a measure of how far your model’s predictions have deviated from the actual outcome. The rule of thumb is that high bias equals too basic of a model.
  • Variance focuses on dynamics — it lets us know the movement of changes during training on slightly different data. A model with high variance might handle a greater variety of training data sets, but it may create new patterns each time.

There should also be a balance between these two concepts — they are not mutually exclusive. Usually, we face the following phenomena: an algorithm has a low bias and a high variance (it is overfitted). Another example may have a high bias and a low dispersion (it will indicate insufficient training). As you train the model longer, the bias usually decreases, and the variance goes up. As we can see, they often contradict each other, but in an ideal world, they should be balanced.

As you begin to find the balance, finer tuning of your models becomes available to you. Thus, you will avoid two issues of overfitting vs underfitting at once, and at the same time, you will be able to optimize the algorithms.

Striking a Balance: Overfitting vs Underfitting in ML

How to Prevent Overfitting?

Overfitting changes the course of your products for the worse. No business owner wants to waste money on models that don’t work. That is why it is worth understanding what are the ways to overcome such problems. Let’s take a look at the main strategies:

  • First of all, you may try to improve the amount of data. If we feed the model more data, it learns complex relationships between inputs and outputs. However, the priority remains the quality of the information. Give preference only to accurate and verified sources.
  • Also, pay attention to how you add information. It is one of the best ways to avoid conflict over details — if the model receives the same data simply in a different form, it theoretically saves you from overfitting problems.
  • Do not forget about the balance of the training process. It’s a double-edged sword — stop training early, and the model will be too basic. You will not be able to stop in time — and the model will be too focused on one thing. The recipe for success is balance.

So, if you are looking for ways to help you generalize the knowledge of machine learning algorithms, but at the same time, they will not be fixated on details, such tips may come in handy.

How to Prevent Underfitting?

Underfitting isn’t a dead end. We have several tools at our disposal to overcome the challenge:

  • Give it more time to learn. This will allow it to explore the nuances of the data more deeply. But remember, training for too long contributes to overfitting.
  • Navigate the complexity. Less intricate models might struggle to grasp the subtleties in the information, which may give rise to misinterpretations. A more complex one handles unexpected data points better. The wider range of understanding lets the algorithm predict accurately based on new information it hasn’t seen before.
  • Minimize the use of regularization. However, overly strict regularization hinders the model’s power to mature effectively. Regularization needs a balanced touch. Reverting to it complicates the model — it potentially leads to improved learning outcomes.

So, if you fully understand the concepts and use the provided strategies, you’re better equipped to tackle common ML challenges. You will have access to the power of ML to solve real-world issues and make informed decisions.

A Good Fit in Machine Learning

Do you want your model to achieve its full potential? Then, you must navigate a critical tightrope walk: striking a balance between overfitting vs underfitting. It hinges on finding the fine line between bias and variance. An excellent fit model exhibits a close correspondence between its predicted and the actual values it encounters.

Imagine this visually with an overfitting vs underfitting graph — the optimal zone lies in the middle.

So, how do we achieve the optimal state?

A key strategy involves leveraging dedicated validation sets. The specialized data pools serve as a crucial testing ground for fine-tuning the model. Validation sets evaluate the model’s performance on brand-new datasets to let us select the most suitable model configuration.

Another powerful tool in our arsenal is resampling techniques. They involve building and training a model on several independent subsets of the input data. Through the process, we evaluate the consistency of the model across different data samples. Resampling methods instill confidence in the model’s ability to generalize effectively regardless of the specific training data used.

As we implement the strategies, we guide our models away from the complications of overfitting vs underfitting.

Summing Up

To create reliable ML models, the most vital thing is to eliminate the dangers of overfitting vs underfitting. How to do it? We need to understand the interaction between bias and variance — only then can we strategically apply methods to complete a well-fitting model. GlobalCloudTeam’s AI experts have the expertise to help you through the process. Our team creates models that learn effectively from data and deliver optimal results.

Alex Johnson

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