Welcome to the first unit of our course on handling unbalanced datasets. Today, we'll focus on identifying a common issue in machine learning: data imbalance. Our goal is to understand what data imbalance is, why it can be misleading, and how to spot it in your datasets. By the end of this lesson, you'll be able to recognize when a dataset is unbalanced and understand why relying solely on accuracy can lead to false confidence in your model's performance.

Before we dive deeper, let's quickly recall the dataset we've been working with. It includes the following features:

• age
• income
• credit_score
• num_purchases
• time_on_site
• num_visits
• gender
• region
• device
• preferred_category
• membership_status
• referral_source
• label.

The data is information about customers. The label is if the customer made a purchase or not, where 1 corresponds to a purchase was made. Our goal is to train a model to predict if a new customer will make a purchase or not.

In the last practice of the previous course, we stumbled across a strange situation: while our model's accuracy was almost 100%, it failed to predict label 1 correctly in most of the cases. It happened due to high data imbalance: most of the site visitors never make the purchase, so the distribution between labels is shifted towards 0.

When a dataset is unbalanced, models tend to focus on predicting the majority class correctly, since that minimizes the overall error. As a result, the model may simply learn to always predict the majority class (in this case, label 0), ignoring the minority class (label 1). This leads to poor performance on the minority class, which is often the class we care about most.

Let's consider another example. Imagine a fraud detection system where only 1% of transactions are fraudulent. If your model predicts every transaction as non-fraudulent, it would achieve 99% accuracy, which sounds impressive but is misleading because it fails to identify any fraudulent transactions.

A dataset is considered unbalanced if one class significantly outnumbers the other(s). A common rule is that if one class makes up less than 10% of the dataset, it is likely unbalanced. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class, which is often the class of interest.

Let's use Python to identify data imbalance in our dataset. We'll use pandas to read a CSV file and analyze the label distribution. Here, pandas.read_csv() loads our dataset, and value_counts() counts the occurrences of each label. This simple analysis helps us quickly see if one label is more common than the other, indicating potential imbalance.

Visualizing data distribution can provide a clearer picture of any imbalance. Let's use matplotlib to create a bar plot of our label distribution. This plot visually represents the number of samples for each label. If one bar is significantly taller, it indicates an imbalance. Visualizations like this are crucial for understanding your data and making informed decisions about handling imbalance.

Now that we've printed the counts and plotted the distribution, let's interpret the results. If one label has far fewer samples, your dataset is unbalanced. This imbalance can lead to a model that performs well on the majority class but poorly on the minority class, which is often the class of interest.

For example, in a medical diagnosis dataset where only 5% of patients have a rare disease, a model that always predicts "no disease" will be correct 95% of the time, but it will fail to identify any actual cases of the disease. This is a serious problem if the minority class is the one you care about detecting.

Instead of relying on accuracy, which can be misleading, consider using metrics like precision, recall, or the F1 score. These metrics provide a more balanced view of your model's performance, especially with unbalanced datasets.

Let's shortly remind ourselves what these metrics are:

• Precision: The ratio of true positive predictions to the total predicted positives. It indicates how many of the predicted positive cases were actually positive.
• Recall: The ratio of true positive predictions to the total actual positives. It measures how well the model identifies positive cases.
• F1 Score: The harmonic mean of precision and recall, providing a single metric that balances both concerns.

In this lesson, we've explored data imbalance and why it can be misleading when evaluating model performance. We've learned how to identify imbalance using Python and visualize it with matplotlib. Recognizing data imbalance is the first step in addressing it effectively.

Ready for practice? Now that you've grasped the theory, it's time to put your knowledge into practice. In the upcoming exercises, you'll explore the dataset, identify imbalance, and experiment with different metrics to evaluate model performance. Let's get started!