IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Ensemble learning refers to a technique in machine learning that combines multiple models to improve overall performance. The idea behind ensemble learning is that by aggregating the predictions of several models, the combined result often has higher accuracy and robustness compared to individual models. This is because different models may capture different patterns in the data, and thus their collective judgment leads to better generalization to unseen data.

In ensemble methods, common techniques include bagging and boosting. In bagging, multiple models are trained in parallel, and their predictions are averaged to reduce variance. In boosting, models are trained sequentially, where each model attempts to correct errors made by the previous ones, improving accuracy. This approach leverages the strengths of various models, thereby addressing issues like overfitting and variance that can be present in single model approaches.

The other options do not accurately reflect the nature of ensemble learning. While one might think that a method using a single model (the first option) could be effective, ensemble learning specifically distinguishes itself by relying on multiple models. The third option, which involves splitting data into training and testing sets, is a fundamental step in the machine learning process but does not pertain directly to ensemble learning. Similarly, the option regarding linear regression analysis mentions a specific type