Question: Differentiate between bagging and boosting.Answer: Bagging (Bootstrap Aggregating) and boosting are ensemble learning techniques. Bagging builds multiple models independently and combines them, while boosting builds models sequentially, giving more weight to misclassified instances. |
復習用に保存
この項目をブックマークに追加したり、難しい内容としてマークしたり、復習セットに入れたりできます。
役に立ちましたか？ はい いいえ
ユーザー評価で最も役立つ内容:
- Explain the concept of feature engineering.
- What is the purpose of regularization in machine learning?
- Explain the term \'hyperparameter\' in the context of machine learning.
- What is the purpose of the activation function in a neural network?
- Explain the term \'precision\' in the context of classification.