The correct answer is D. Model stacking.
Model stacking is a machine learning technique that combines the predictions of multiple models to produce a more accurate prediction than any of the individual models could produce on its own. This is done by creating a meta-model that takes the predictions of the individual models as input and produces a single prediction as output.
Model stacking can be used to improve the accuracy of predictions in a variety of situations, such as when the data is noisy or when the individual models are not very accurate. It can also be used to improve the interpretability of predictions, by providing a way to understand how the individual models are contributing to the overall prediction.
Here is a brief explanation of each option:
- Model visualization is a technique that uses graphical representations to help understand the relationships between the features in a dataset and the predictions of a model. This can be useful for identifying potential problems with the model, such as overfitting or underfitting.
- Data augmentation is a technique that artificially increases the size of a dataset by creating new data points that are similar to the existing data points. This can be useful for improving the accuracy of models that are trained on small datasets.
- Data imputation is a technique that fills in missing values in a dataset. This can be useful for improving the accuracy of models that are trained on datasets with missing values.