Majority Class is a term that refers to a class or segment with the maximum number of instances or observations in a dataset.
For instance, if a dataset of customer reviews for a product has 70% positive and 30% negative reviews, the positive reviews are from the majority class. The remaining 30% is the minority class.
Sometimes, the majority class can completely dominate the dataset to the extent of distorting the model’s accuracy. Such an outcome can occur if the dataset is imbalanced with more instances of one class than the others. This can lead to high accuracy for the majority class and low accuracy for the minority class. This is because the model predicts the majority class for most instances.
Therefore, when designing AI and ML algorithms, it is important to consider imbalanced datasets and devise solutions to tackle problems like oversampling and undersampling.
Applications of Majority Class in AI
1. Baseline performance evaluation
Majority class serves as a parametric baseline to assess the model’s performance and accuracy compared to it.
2. Imbalance data classification
In many real-world datasets, one class may have more samples than the others. Here again, the majority class serves as a reference point to compare how models perform in imbalance classification tasks.
3. Exploratory data analysis
Majority class assists in feature engineering by identifying imbalance classes and understanding the distribution of classes in the dataset. This step is beneficial for data science projects.
4. Prevent bias in models
It is important to train models to remain unbiased towards any particular class. In such cases, majority class checks if the model is biased and adjusts its training to reduce the biased nature.
5. Data preprocessing
Preprocessing is a crucial preliminary step in preparing data to train a model. By downsampling or filtering out the majority class, the balance is restored to the dataset, and overfitting is avoided.
6. Synthetic data generation
Synthetic data generation is a technique that generates new data samples to augment existing datasets. Here, majority class is used to create synthetic data for classes that are underrepresented. This helps balance the dataset and enhance the model’s performance.
Related terms
