AM I RACIST?

BY MOHAMMAD TRAD

A person tends to issue false judgements about certain subjects when he does not have enough information about it, and these exact same false judgements can happen to our AI models when they’re fed to imbalanced data, read along to find out how dangerous it can be and how can we avoid it!

Unraveling the Imbalanced Data Conundrum

Just like many aspects of life, data doesn’t always present itself in a neat 50-50 division. The phenomenon of imbalanced data arises when one category significantly overshadows another in a classification scenario. Take fraud detection as an example, where fraudulent transactions are just a tiny portion of the total transactions, resulting in a highly skewed data set. This imbalance can trigger a series of challenges that potentially undermine the performance of machine learning models.

But the Question is, How Does the Machine Learning Model See Imbalanced Data?

Imagine a classroom where one student is shouting louder than the rest. The teacher might end up listening to only that student, failing to hear others who have valuable insights. This situation mirrors the challenge posed by an imbalanced dataset in machine learning. When a model is trained on such a dataset, it tends to be biased towards the majority class, effectively silencing the minority class. Consequently, while the model excels at predicting outcomes for the majority class, it falls short when it comes to recognizing instances of the minority class.

The Motivation for Tackling Imbalanced Data

Imbalanced data can have a big effect on building models. Think about it this way: in healthcare, not handling imbalanced data could lead to misdiagnosing serious conditions, risking lives. In finance, it could mean big money losses from fraud that slips through. So, dealing with data imbalance isn’t just important, it’s urgent! And remember, every step we take to improve this makes our world a safer and healthier place.

Strategies when Dealing with Imbalanced Data

Here are some steps and strategies that may help in improving the performance of your machine learning model and dealing with your data in the best possible way:

1. Collect more data: If this is feasible, it can be an excellent option and a perfect solution for imbalanced data. However, in many situations, collecting more data is not a viable choice. What should you do?

2. Change performance metric: Changing the performance metric can be a game-changer when dealing with imbalanced data. While traditional metrics like accuracy might give you a false sense of success by favoring the majority class, alternative metrics like Precision, Recall, F1-score, or AUC-ROC offer a more balanced picture.

3. Try Different Algorithms: As with most Data science problems, it’s always good practice to try a few different suitable algorithms on the data. There are many types of machine learning algorithms that seem to be effective with imbalanced dataset problems. Some of these methods include:

a. Ensemble Methods

b. Gradient Boosting methods

c. K-NN

d. SVM

4. Resampling: This involves either oversampling the minority class or undersampling the majority class to achieve a more balanced dataset.

In the vast universe of machine learning, achieving balance is not just a nice-to-have; it’s a necessity. It’s only when our data is balanced that our models can truly flex their predictive muscles and make a real difference in tackling real-world challenges. So, let’s not forget, balance in data is the secret ingredient to unleashing the full power of our models.