如何在机器学习中更加重视某些功能? [英] How to put more weight on certain features in machine learning?

问题描述

如果使用像scikit-learn这样的库，如何在输入中的某些特征上为SVM等分类器分配更多权重?这是人们做的事情，还是我的问题有其他解决方案?

If using a library like scikit-learn, how do I assign more weight on certain features in the input to a classifier like SVM? Is this something people do or is there another solution to my problem?

推荐答案

首先-您可能不应该这样做.机器学习的整个概念是使用统计分析分配最佳权重.您在这里干扰了整个概念，因此您需要非常有力的证据，这对于您要建模的过程至关重要，并且由于某种原因，您的模型目前缺少它.

First of all - you should probably not do it. The whole concept of machine learning is to use statistical analysis to assign optimal weights. You are interfering here with the whole concept, thus you need really strong evidence that this is crucial to the process you are trying to model, and for some reason your model is currently missing it.

话虽如此-没有普遍的答案.这纯粹是特定于模型的，其中一些可以使您加权特征-在随机森林中，您可以使从中抽样特征的分布偏向于对感兴趣的特征进行分析；在SVM中，只需将给定特征乘以一个常数就足够了-还记得何时被告知要在SVM中对特征进行规范化吗?这就是为什么-您可以使用要素的比例将分类器导向"给定的要素.那些高价值的将被优先考虑.实际上，这将适用于任何权重范数正则化模型(正则逻辑回归，岭回归，套索等).

That being said - there is no general answer. This is purely model specific, some of which will allow you to weight features - in random forest you could bias distribution from which you sample features to analyse towards the ones that you are interested in; in SVM it should be enough to just multiply given feature by a constant - remember when you were told to normalize your features in SVM? This is why - you can use the scale of features to 'steer' your classifier towards given features. The ones with high values will be preffered. This will actually work for any weight norm-regularized model (regularized logistic regression, ridge regression, lasso etc.).

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