Binary classification problem : Imbalanced classes

A binary classification problem with imbalanced classes
- is a common issue in machine learning
- where the number of examples in one class significantly outnumbers the examples in the other class.

- This can lead to poor performance when using a standard machine learning algorithm.

There are few ways to approach this problem:

Resampling: Balance the class distribution by either
- oversampling the minority class or undersampling the majority class.

Cost-Sensitive Learning: Assign higher misclassification costs to
- the minority class to make the algorithm more sensitive to it.

Ensemble Methods: Use ensemble methods such as bagging or boosting
- to give more weight to the minority class examples.

Synthetic Data Generation: Generate synthetic samples
- for the minority class to balance the class distribution.

Model Selection: Choose a machine learning algorithm
- that is robust to imbalanced data, such as decision trees or random forests.

Evaluation Metrics: Use metrics that are sensitive to class imbalance,
- such as precision, recall, F1-score, or the area under
-the receiver operating characteristic curve (AUC-ROC).