Final answer:
Undersampling and oversampling are resampling strategies used to balance class distribution in datasets, thereby potentially improving machine learning model performance. Undersampling can simplify the majority class at the risk of information loss, while oversampling increases the representation of the minority class, which may lead to overfitting.
Step-by-step explanation:
Undersampling and oversampling are techniques used to address the problem of unbalanced classes in machine learning, which can affect model predictions. Undersampling reduces the size of the majority class by removing instances, leading to a more balanced dataset, potentially at the cost of losing important data from the majority class. This can help models trained on the data generalize better but may also increase the chance of overfitting if the reduced dataset doesn’t represent the true underlying population. Oversampling, on the other hand, increases the size of the minority class by duplicating existing instances or generating new instances through techniques like SMOTE (Synthetic Minority Over-sampling Technique). This adds more examples from the minority class to the training set, which can help in learning their patterns better but can also lead to overfitting if the model learns the noise in the data.
The impact of these resampling techniques on predictions depends on several factors, including the extent of the imbalance, the nature of data, and the classification model used. By achieving a more balanced class distribution, models may perform better on minority classes, but care must be taken to avoid introducing bias or artifacts that can degrade the model’s performance on unseen data.