[英]Is 'feature extraction' a core machine learning task?
I have been arguing with a friend about 'feature extraction'.我一直在和一个朋友争论“特征提取”。 He says the main task of ML is to extract features.
他说 ML 的主要任务是提取特征。 But I disagree.
但我不同意。 In a common-sense feature extraction is not an ML task.
在常识中,特征提取不是 ML 任务。 If we consider wx+b as the simplest way to represent ML, the task of ML is to find the best w and b.
如果我们认为 wx+b 是表示 ML 的最简单方式,那么 ML 的任务就是找到最好的 w 和 b。 x is the feature.
x 是特征。 ML tries to find out the best w and b values for a given x, it matches with the training data and thus learns how to find w and b.
ML 试图找出给定 x 的最佳 w 和 b 值,它与训练数据匹配,从而学习如何找到 w 和 b。
My friend says it is the core task of ML to extract features.我朋友说提取特征是ML的核心任务。 But as I know feature extraction is a data preprocessing task mainly.
但据我所知,特征提取主要是一项数据预处理任务。
Extracting features is an important job in ML.提取特征是机器学习中的一项重要工作。 Without features you cannot find the best "w" and "b".
没有特征,你就找不到最好的“w”和“b”。 If you are able to find w and b without feature extraction then you don't really need to go forward with ML.
如果您能够在不提取特征的情况下找到 w 和 b,那么您真的不需要继续使用 ML。
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