To maximize the margin between classes
To minimize prediction errors
To perform unsupervised learning
To visualize data relationships
Answer is Right!
Answer is Wrong!
The correct answer is: B. To minimize prediction errors.
Gradient boosting is a machine learning technique for regression and classification problems that uses a series of weak learners to build a strong learner. The weak learners are trained sequentially, with each learner trained to correct the errors made by the previous learners. This process is repeated until the desired level of accuracy is reached.
Gradient boosting is a powerful technique that has been shown to be effective on a variety of problems. It is particularly well-suited for problems where the data is noisy or where the underlying relationship between the features and the target variable is complex.
The other options are incorrect for the following reasons:
- Option A is incorrect because the goal of gradient boosting is not to maximize the margin between classes. The goal of gradient boosting is to minimize prediction errors.
- Option C is incorrect because gradient boosting is a supervised learning technique. Supervised learning techniques require labeled data, while unsupervised learning techniques do not.
- Option D is incorrect because gradient boosting is not a technique for visualizing data relationships. There are other techniques that are better suited for this task, such as dimensionality reduction and clustering.