IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Ensemble learning is a technique that combines multiple models to produce a better predictive performance than a single model alone. Random Forests is a classic example of ensemble learning because it builds a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees.

In Random Forests, each decision tree is trained on a random subset of the training data, which introduces diversity among the trees. This diversity helps to reduce overfitting and improve the overall generalization of the model. By averaging the predictions from multiple trees, Random Forests can achieve higher accuracy and robustness compared to a single decision tree.

While neural networks and decision trees can be part of ensemble methods, they are not ensemble methods by themselves. They can be combined in ensemble frameworks, but they are typically viewed as individual models. K-Means Clustering, on the other hand, is a clustering algorithm and unrelated to ensemble learning, as it does not combine multiple predictive models but rather partitions data into clusters.

Therefore, Random Forests stands out as a widely used ensemble learning method due to its effectiveness in dealing with various problems in data science.