Which of the following is a common method for handling imbalanced datasets in cl

Which of the following is a common method for handling imbalanced datasets in classification problems?

Which of the following is a common method for handling imbalanced datasets in classification problems?

Step 1: Understand what an imbalanced dataset is. This means one class has many more examples than another class.
Step 2: Recognize that this imbalance can make it hard for a model to learn about the less common class.
Step 3: Learn about oversampling, which is a technique to help balance the classes.
Step 4: Oversampling involves adding more examples to the minority class, so it has more data for the model to learn from.
Step 5: By using oversampling, the model can improve its ability to predict the minority class correctly.

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