Databricks Certified Machine Learning - Associate
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In the context of AutoML, explain the role of ensemble learning and how AutoML can leverage multiple models to improve the performance of the final预测 model. Provide a detailed explanation of the techniques used by AutoML for ensemble learning and their significance in reducing overfitting and improving model generalization.
A
AutoML does not support ensemble learning, as it relies on individual models for each task.
B
AutoML supports ensemble learning by manually selecting a group of models and combining their predictions using a voting or averaging mechanism.
C
AutoML supports ensemble learning by using techniques such as bagging, boosting, and stacking to combine the predictions of multiple models and improve the performance of the final预测model.
D
AutoML supports ensemble learning by automatically selecting the best individual model and using it as the final prediction model, without combining the predictions of multiple models.