Machine Learning 面接の質問と回答
質問 6. Differentiate between bagging and boosting.
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.
質問 7. What is the purpose of the activation function in a neural network?
The activation function introduces non-linearity to a neural network, allowing it to learn complex patterns. Common activation functions include sigmoid, tanh, and ReLU.
質問 8. Explain the term 'precision' in the context of classification.
Precision is the ratio of correctly predicted positive observations to the total predicted positives. It is a measure of the accuracy of positive predictions made by a classification model.
質問 9. What is the curse of dimensionality?
The curse of dimensionality refers to the challenges and issues that arise when working with high-dimensional data. As the number of features increases, the data becomes sparse, and the computational requirements for training models grow exponentially.
質問 10. How does a decision tree work?
A decision tree is a tree-like model where each node represents a decision based on a feature, and each branch represents an outcome of that decision. It is used for both classification and regression tasks.
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.