Machine Learning 面试题与答案
问题 26. How does the term 'bias' and 'variance' relate to model error in machine learning?
Bias refers to the error introduced by approximating a real-world problem with a simplified model. Variance is the amount by which the model's prediction would change if it were estimated using a different training dataset. The bias-variance tradeoff aims to balance these two sources of error.
问题 27. What is transfer learning, and how is it used in deep learning?
Transfer learning is a technique where a pre-trained model on a large dataset is adapted for a different but related task. It allows leveraging knowledge gained from one domain to improve performance in another, often with smaller amounts of task-specific data.
问题 28. Explain the concept of ensemble learning.
Ensemble learning combines the predictions of multiple models to improve overall performance. Common ensemble techniques include bagging, boosting, and stacking. The idea is that the combination of diverse models can provide better results than individual models.
问题 29. What is the purpose of a confusion matrix in the context of classification?
A confusion matrix is a table that summarizes the performance of a classification algorithm. It shows the number of true positives, true negatives, false positives, and false negatives, providing insights into the model's accuracy, precision, recall, and other metrics.
问题 30. Explain the concept of kernel functions in support vector machines (SVM).
Kernel functions in SVM enable the algorithm to operate in a higher-dimensional space without explicitly calculating the new feature space. They transform the input data into a higher-dimensional space, making it easier to find a hyperplane that separates different classes.
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