Data Preprocessing Questions
The common techniques used for data balancing are:
1. Undersampling: This technique involves reducing the majority class by randomly removing instances from it until it is balanced with the minority class.
2. Oversampling: This technique involves increasing the minority class by replicating or creating new instances until it is balanced with the majority class. This can be done through techniques like random oversampling, SMOTE (Synthetic Minority Over-sampling Technique), or ADASYN (Adaptive Synthetic Sampling).
3. Hybrid methods: These methods combine both undersampling and oversampling techniques to achieve a balanced dataset. Examples include SMOTEENN (SMOTE + Edited Nearest Neighbors) and SMOTETomek (SMOTE + Tomek Links).
4. Cost-sensitive learning: This technique assigns different misclassification costs to different classes, giving more weight to the minority class. This encourages the model to pay more attention to the minority class during training.
5. Ensemble methods: These methods involve training multiple models on different balanced subsets of the data and combining their predictions. This can help in handling imbalanced data by reducing bias towards the majority class.
6. Data augmentation: This technique involves generating new synthetic data points by applying transformations or perturbations to the existing data. This can help in increasing the size of the minority class and improving the overall balance of the dataset.
It is important to note that the choice of data balancing technique depends on the specific characteristics of the dataset and the problem at hand.