Lesson 131: Addressing Data Imbalance

Recap: Applications of Dimensionality Reduction

In the previous lesson, we learned about dimensionality reduction techniques like t-SNE and UMAP, which transform high-dimensional data into lower dimensions for better visualization and improved model performance. Dimensionality reduction helps understand complex data structures and prevents model overfitting.

Today, we will focus on a common issue in machine learning: Data Imbalance. We will explore how to address this problem using sampling techniques to balance the distribution between majority and minority classes.

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What is Data Imbalance?

Data Imbalance occurs when the distribution of data between different classes in a classification task is significantly unequal. For instance, if one class (majority class) has a much larger amount of data than another class (minority class), the model may become biased toward the majority class, leading to poor performance on the minority class.

Example: Understanding Data Imbalance

Data imbalance is like a sports game where one team has 10 players and the other team has only one. As the game progresses, the team with more players has a significant advantage, while the other team struggles. Similarly, in machine learning, a model trained on imbalanced data will likely favor the majority class, leading to biased outcomes.

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Methods for Addressing Data Imbalance

To solve data imbalance issues, resampling techniques are effective. Two primary methods are under-sampling and over-sampling, which adjust the class balance by either reducing or increasing data points.

1. Under-Sampling

Under-Sampling involves reducing the number of data points in the majority class to match the size of the minority class. This method improves data balance, encouraging the model to focus more on the minority class during training.

Advantages of Under-Sampling

• Reduces the overall dataset size, speeding up model training.
• Lowers computational costs, saving resources.

Disadvantages of Under-Sampling

• Important information from the majority class may be lost.
• With less data, the model might not learn effectively.

Example: Understanding Under-Sampling

Under-sampling is like reducing the number of players on the larger team to match the smaller team. This creates balance, but reducing too many players may result in losing key team members, affecting the overall performance.

2. Over-Sampling

Over-Sampling increases the size of the minority class by duplicating data points or generating new ones. This method helps the model learn more about the minority class, ensuring it is not overlooked.

Advantages of Over-Sampling

• Increases the amount of minority class data, ensuring the model learns from it effectively.
• Maximizes data usage without removing valuable information.

Disadvantages of Over-Sampling

• Duplicating data can lead to overfitting, where the model performs well on training data but poorly on new data.
• The dataset size increases, potentially raising computational costs.

Example: Understanding Over-Sampling

Over-sampling is like adding more players to the smaller team to match the larger one. This helps balance the game, but if the same players are duplicated repeatedly, it may limit strategic diversity.

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Practical Sampling Techniques

1. Random Sampling

The simplest method is to randomly select and resample data points. In under-sampling, random data points from the majority class are removed, while in over-sampling, random data points from the minority class are duplicated.

• Random Under-Sampling: Randomly removes data points from the majority class.
• Random Over-Sampling: Randomly duplicates data points from the minority class.

While random sampling is straightforward, it risks ignoring important data patterns, making it unsuitable for complex datasets.

2. Stratified Sampling

Stratified Sampling divides the dataset based on class categories, ensuring that each class is proportionally represented when sampling. This method helps maintain the characteristics of each class while balancing the dataset.

3. Synthetic Sampling

Synthetic Sampling involves generating new data for the minority class. One popular technique is SMOTE (Synthetic Minority Over-sampling Technique), which creates new data points by interpolating between existing minority class samples, increasing diversity and balance.

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Choosing the Right Sampling Method

Selecting the appropriate sampling method depends on the dataset’s size and the model’s characteristics.

• If the minority class data is valuable, and you want to retain all information, over-sampling is suitable.
• If the dataset is large, and resource efficiency is needed, under-sampling is effective.
• To maintain diversity between classes, consider synthetic or stratified sampling.

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Conclusion

In this lesson, we explored Data Imbalance and the techniques used to address it. Under-Sampling and Over-Sampling help balance majority and minority classes to improve model accuracy. Next, we will dive into SMOTE, a widely used method for generating new data to address imbalance effectively.

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Next Topic: SMOTE for Over-Sampling

In the next lesson, we will cover SMOTE (Synthetic Minority Over-sampling Technique), explaining how to generate new data points for the minority class to resolve imbalance.

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Notes

1. Data Imbalance: A state where there is a significant difference in the amount of data between classes.
2. Under-Sampling: Reducing the number of data points in the majority class to balance the dataset.
3. Over-Sampling: Increasing the number of data points in the minority class to balance the dataset.
4. Stratified Sampling: Ensures proportional representation of each class during sampling.
5. SMOTE: A synthetic sampling technique that generates new data points by interpolating between existing minority class samples.