The Role of the Confusion Matrix in Addressing Imbalanced Datasets

Classification algorithms are some of the most useful machine learning models in use today. However, training them to deliver reliable real-world results can be challenging, especially when you must deal with imbalanced datasets.

Many classification tasks naturally involve imbalance. Fraud cases are not as common as normal behavior, acceptable parts are more common than defects, and an authorized user will use facial recognition more than unauthorized people. As a result, you won’t have equal sample sizes for all scenarios if you’re training your model on real-world data, presenting reliability concerns. A confusion matrix can help.

What Is a Confusion Matrix?

A confusion matrix is a chart that compares the predicted labels of a classification algorithm to their actual value. It does this by plotting results along four categories:

• True positives
• True negatives
• False positives
• False negatives

In a business environment where 80% of AI projects fail, you must assess a model’s performance before putting it to work. Confusion matrices do just that for classification algorithms. While they may not reveal the cause behind any issues, they offer a visual representation of how the labels an algorithm assigns to inputs compare to reality.

Confusion matrices are relatively simple in their operation. You take a dataset where you know all the correct labels and feed the data to the model without its labels. Once the algorithm determines where each falls in the binary, you compare these results to the real values. True positives and negatives occur when the predicted value aligns with the actual label, and the inverse applies to false outcomes.

Why Use a Confusion Matrix?

As simple as they are, confusion matrices are an essential tool for building and training a classification model with imbalanced datasets. You can use them to gauge a few key metrics.

Ensuring Model Accuracy

The most obvious application of a confusion matrix is to determine your algorithm’s accuracy. Classification models must be able to identify true positives to be useful in a business context. A reliable audience segmentation tool, for example, increases the chances of converting leads into customers, so higher accuracy delivers tangible monetary results.

Running a given dataset through a confusion matrix won’t improve the model’s accuracy, but it will gauge it. You can use it to see how your algorithm performs with imbalanced data before deciding if it needs refinement or further training or if it’s ready for deployment.

Improving Model Precision

Similarly, you can use confusion matrices to evaluate a model’s precision. Whereas accuracy looks at overall correctness, precision compares true positives to the total of both true and false positives. A lower ratio indicates a relatively high false positive rate, which has serious implications in the real world.

False positives in a security monitoring tool lead to high alarm volumes and worsening alert fatigue, which 62% of cybersecurity professionals say has led to turnover. Similar issues in a fraud detection algorithm could lock authorized users from using their credit cards. Given such outcomes, you must ensure your model exhibits low false positives before rolling it out, making confusion matrices all the more crucial.

Minimizing Model Bias

A confusion matrix can also be a helpful way to recognize AI bias. This is a prominent issue in classification algorithms — one study found facial recognition models are up to 100 times less accurate for African Americans than Caucasians. You can highlight such discrepancies by comparing the confusion matrix for a dataset for one demographic against another.

Using confusion matrices this way requires multiple passes — you have to compare the results between datasets, each focused on a different demographic. While that may take time, it lets you detect bias before applying your model in the real world. Consequently, you can correct the issue before it causes larger problems.

Considerations When Using a Confusion Matrix

One thing to keep in mind when using a confusion matrix is that it only works when you know your dataset’s real values. That means you’ll need to label all your data if you don’t already have a labeled set. Considering data preparation is typically the longest phase in AI development, you should look for some time-saving opportunities here.

The fastest way to approach the issue is to use publicly available labeled datasets. Depending on the type of classification model you’re building, you should be able to find some open-source options.

Alternatively, you may need to pay for the data or gather it yourself. In these cases, you should adjust project timelines and budgets accordingly to provide enough time and money for validation. Remember, it’s better to use a confusion matrix than forgo the complication, considering the importance of ensuring accuracy and precision.

While most confusion matrix examples involve binary classifications, you can use one for multi-class applications, too. Simply add another row and column for each new value. For example, you may divide results into positive, negative, and neutral. Keep in mind, though, that the more values you add, the longer and more work-intensive the process will be.

Get More Out of Your Classification Algorithm

Imbalanced data is virtually impossible to avoid in many classification tasks. However, you can overcome it if you learn how to apply a confusion matrix.

Confusion matrices are not a perfect solution — they won’t tell you where an issue comes from or how to fix it. Still, they are indispensable as a way to recognize a problem you may otherwise miss. Once you learn to verify your algorithm’s reliability or highlight its shortcomings, you can create more functional models for you or your clients.