What is the primary reason overfitting is problematic in machine learning?

Question

BlackTom AI · Accepted Answer

Exploring the concept of overfitting, we consider what goes wrong when a model fits the training data too closely.
Option 1: 'It causes poor generalization on unseen data' — This is the core issue. When a model captures noise and idiosyncrasies of the training set, its performance drops on new, unseen data because those patterns aren’t truly representative.
Option 2: 'It improves prediction accuracy' — In the short term, it might appear to perform very well on the training data, but this does not translate to better performance on new data. The hallmark of overfitting is a gap between training accuracy and test accuracy, with test accuracy often being worse.
Option 3: 'It leads to simpler models' — Overfitting is typically a symptom of models being too complex relative to the data, not of simpler models. Simpler models tend to underfit, missing underlying patterns, rather than overfit to noise.
Option 4: 'It underestimates the data' — This phrasing is ambiguous, but in the context of overfitting, the problem is not about underestimating the data; it’s about modeling noise as if it were signal, which harms generalization rather than underestimating the dataset.
To summarize the missteps: overfitting causes the model to rely on noise in the training data, which reduces its ability to generalize. The other options either describe effects opposite to overfitting (like improved test performance or simplification) or use unclear wording that does not capture the core problem of poor generalization.

What is the primary reason overfitting is problematic in machine learning?单项选择题

类似问题

An unpruned decision tree achieves 99% accuracy on training data but only 60% on test data. What is this phenomenon called?

In the above image, the prediction made at any value of X is shown by the blue line. This predictive model is an overfit for the training data.

Which one of the following linear discriminants is most prone to overfitting a training data set?

Overfitting (1 mark)What is overfitting?[Fill in the blank]

Which of the following conditions can lead to logistic regression overfitting the training data?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单