The correct answer is: A. Model stacking
Model stacking is a machine learning technique that combines the predictions of multiple models to produce a more accurate prediction. It is often used in cases where the individual models are not very accurate, but where the combined prediction is more accurate.
There are two main types of model stacking:
- Bagging: In bagging, each model is trained on a different subset of the data. This helps to reduce the variance of the individual models, and hence the variance of the combined prediction.
- Boosting: In boosting, each model is trained to correct the errors of the previous models. This helps to reduce the bias of the individual models, and hence the bias of the combined prediction.
Model stacking can be used to combine any type of model, including classifiers, regressors, and clustering algorithms. It is a powerful technique that can be used to improve the accuracy of many machine learning models.
Model combining is a general term that can refer to any technique that combines the predictions of multiple models. This includes model stacking, as well as other techniques such as voting and averaging.
Model structuring is a more specific term that refers to techniques that combine the predictions of multiple models by creating a new model that is based on the individual models. This can be done in a number of ways, such as by using a meta-model or by using a Bayesian network.
In conclusion, the correct answer to the question “Which of the following method can be used to combine different classifiers?” is A. Model stacking.