The correct answer is D. Ensemble model.
An ensemble model is a type of machine learning model that combines the predictions of multiple models to make a more accurate prediction. This is done by training multiple models on the same data set and then averaging or voting on their predictions. Ensemble models can be used for both classification and regression tasks.
A random model is a type of machine learning model that makes predictions based on random chance. This type of model is not very accurate and is not typically used in practice.
A regression model is a type of machine learning model that predicts a continuous value. For example, a regression model could be used to predict the price of a house or the height of a person.
A supervised model is a type of machine learning model that is trained on labeled data. This means that the model is given a set of data that includes both the input data and the desired output. The model then learns to map the input data to the output data. Supervised models are typically more accurate than unsupervised models.
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