IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The term "boosting" in machine learning specifically refers to an ensemble technique that focuses on combining multiple weak learners to produce a single strong learner. Weak learners are models that perform slightly better than random chance; when they are combined in a sequential manner, boosting methods like AdaBoost or Gradient Boosting help to reduce bias and improve accuracy.

The process involves training weak models in iterations, where each subsequent model is trained to improve upon the errors made by the previous models. By focusing on the instances that prior models misclassified, boosting effectively creates a strong predictive model that leverages the strengths of each individual learner while compensating for their weaknesses. This is a fundamental concept in ensemble methods, particularly in boosting algorithms, which have proven to be highly effective in various machine learning tasks.

The other choices, while relevant to machine learning, do not accurately capture the essence of boosting. For instance, while combining multiple strong learners is a characteristic of ensemble methods, it does not define boosting specifically. Also, boosting is not simply about improving singular models nor is it related to hyperparameter tuning, which involves optimizing settings to enhance model performance.