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Answer: Random Forests, XGBoost, Gradient Boost
Ensemble learning methods combine multiple learning algorithms to improve predictive performance and robustness. Random Forests, XGBoost, and Gradient Boost are all examples of ensemble methods. Random Forests use bootstrap sampling and feature randomness to create a forest of decision trees, XGBoost is an optimized gradient boosting library designed for efficiency and performance, and Gradient Boost builds models in a stage-wise fashion to minimize loss functions. In contrast, Deep & Cross Networks, Decision Trees, and Logistic Regression are not ensemble methods as they rely on single-model approaches. For further reading, refer to: [Towards Data Science](https://towardsdatascience.com/all-machine-learning-algorithms-you-should-know-in-2021-2e357dd494c7)
Author: LeetQuiz Editorial Team
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As a Data Scientist at a leading retail company, you are tasked with improving the accuracy of your predictive models. Initially, you prefer using simple and interpretable algorithms for transparency and ease of explanation to stakeholders. However, when these models do not meet the required performance thresholds, you explore more sophisticated techniques. Your manager recommends ensemble methods to enhance model performance. Given the importance of scalability, interpretability, and performance in your retail environment, which of the following algorithms are considered ensemble methods? (Select 3 options)
A
Random Forests
B
Deep & Cross Networks (DCN)
C
Decision Tree
D
XGBoost
E
Gradient Boost
F
Logistic Regression
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