SVC（支持向量分类），带有分类（字符串）数据作为标签(SVC (support vector classification) with categorical (string) data as la

编程入门行业动态更新时间:2024-10-11 05:28:45

SVC（支持向量分类），带有分类（字符串）数据作为标签(SVC (support vector classification) with categorical (string) data as labels)

我使用scikit-learn来实现一个简单的监督学习算法。本质上我遵循这里的教程（但我自己的数据）。

我尝试适合模型：

clf = svm.SVC(gamma=0.001, C=100.) clf.fit(features_training,labels_training)

但在第二行，我收到一个错误： ValueError: could not convert string to float: 'A'

错误是预期的，因为label_training包含表示三个不同类别的字符串值，例如A ， B ， C 。

所以问题是：如果标记数据表示字符串形式的类别，我如何使用SVC（支持向量分类）。对我来说，一个直观的解决方案似乎只是将每个字符串转换为数字。例如， A = 0 ， B = 1等。但这真的是最好的解决方案吗？

I use scikit-learn to implement a simple supervised learning algorithm. In essence I follow the tutorial here (but with my own data).

I try to fit the model:

clf = svm.SVC(gamma=0.001, C=100.) clf.fit(features_training,labels_training)

But at the second line, I get an error: ValueError: could not convert string to float: 'A'

The error is expected because label_training contains string values which represent three different categories, such as A, B, C.

So the question is: How do I use SVC (support vector classification), if the labelled data represents categories in form of strings. One intuitive solution to me seems to simply convert each string to a number. For instance, A = 0, B = 1, etc. But is this really the best solution?