问题如下:
In the use of machine learning (ML):
选项:
A.some techniques are termed "black box" due to data biases.
B.human judgment is not needed because algorithms continuously learn from data.
C.training data can be learned too precisely, resulting in inaccurate predictions when used with different datasets.
解释:
C is correct.
Overfitting occurs when the ML model learns the input and target dataset too precisely. In this case, the model has been "over trained" on the data and is treating noise in the data as true parameters. An ML model that has been overfitted is not able to accurately predict outcomes using a different dataset and may be too complex.
考点:Machine Learning
解析:这道题考的是机器学习的相关知识。
A选项说一些工具由于数据偏差所以被称为“黑盒子”,这是不正确的。 在我们普通人的角度看起来,机器学习具体的算法并没有一个显性的编程,因为数学家和科学家都已经写好了这个算法,我们只要直接运用就好了,并不知道具体的算法是怎么编写的,就像装在黑盒子black-box一样,而不是说data biases数据偏差。
B选项说人为的判断是不需要的。这个描述是错误的。其实这也是机器学习的缺点,就是算法的选择等仍然需要人类工程师的判断。
C选项说学习到的特征太过于精细反而可能会得到不准确的预测。这其实也是对过度拟合的定义的描述。在机器学习的使用中确实是可能存在这种现象的。所以C选项是正确的。
老师问下 什么事black box? 讲义在哪有讲到呢