The correct answer is D. All of the mentioned.
Bagging is a machine learning ensemble meta-algorithm that combines the predictions of multiple models to produce a more accurate prediction than any of the individual models could produce. It is also known as bootstrap aggregating or bootstrap ensemble.
Bagging works by creating multiple models from a single dataset by sampling with replacement. This means that some data points may be included in multiple models, while others may not be included in any models. The predictions of the individual models are then averaged or combined in some other way to produce a final prediction.
Bagging can be used to improve the accuracy of any type of machine learning model, but it is particularly effective for models that are prone to overfitting. Overfitting occurs when a model learns the training data too well and is unable to generalize to new data. Bagging can help to reduce overfitting by averaging the predictions of multiple models, each of which may have learned different aspects of the training data.
Bagging is a simple and effective way to improve the accuracy of machine learning models. It is widely used in a variety of applications, including spam filtering, fraud detection, and medical diagnosis.
Here is a brief explanation of each option:
- Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm that combines the predictions of multiple models to produce a more accurate prediction than any of the individual models could produce.
- Bootstrap subsetting is a technique used to create a new dataset by sampling with replacement from an existing dataset. This can be used to create multiple datasets from a single dataset, which can then be used to train multiple models.
- Bootstrap predicting is a technique used to predict the value of a target variable by averaging the predictions of multiple models. This can be used to improve the accuracy of predictions, especially for models that are prone to overfitting.