Machine Learning 面试题与答案
问题 21. Explain the concept of cross-entropy loss in the context of classification problems.
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. It penalizes models that are confidently wrong and is a common choice for binary and multiclass classification problems.
问题 22. What is the difference between precision and F1 score?
Precision is the ratio of true positives to the sum of true positives and false positives, while the F1 score is the harmonic mean of precision and recall. F1 score provides a balance between precision and recall, giving equal weight to both metrics.
问题 23. What is gradient boosting, and how does it work?
Gradient boosting is an ensemble learning technique that builds a series of weak learners, typically decision trees, in a sequential manner. Each new learner corrects the errors of the previous ones, producing a strong, accurate model.
问题 24. Explain the term 'feature importance' in the context of machine learning models.
Feature importance measures the contribution of each feature to the predictive performance of a model. It helps identify the most influential features in making predictions and is often used for feature selection and model interpretation.
问题 25. What is the role of a learning rate in gradient descent optimization algorithms?
The learning rate determines the size of the steps taken during the optimization process. It is a hyperparameter that influences the convergence and stability of the optimization algorithm. A too-high learning rate may cause divergence, while a too-low rate may result in slow convergence.
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