SciKit学习中的多类Logistic回归 [英] Multi-Class Logistic Regression in SciKit Learn
问题描述
对于多类案例，我无法正确调用Scikit的Logistic回归.我正在使用lbgfs求解器，并且确实将multi_class参数设置为多项式.
I am having trouble with the proper call of Scikit's Logistic Regression for the multi-class case. I am using the lbgfs solver, and I do have the multi_class parameter set to multinomial.
我不清楚如何在拟合模型时传递真实的类标签.我以为它与随机森林分类器多类相似/相同，您可以在其中传递[n_samples，m_classes]数据帧.但是，这样做时，我得到一个错误，即数据的形状不好. ValueError:错误的输入形状(20，5)-在这个小例子中，有5个类，20个样本.
It is unclear to me how to pass the true class labels in fitting the model. I had assumed that it was similar/same as for the random forest classifier multi-class, where you pass [n_samples, m_classes] dataframe. However, in doing this, I get an error that the data is of a bad shape. ValueError: bad input shape (20, 5) -- in this tiny example, there were 5 classes, 20 samples.
经检查，fit方法的文档说，真值以[n_samples，]形式传递-与我遇到的错误相匹配-但是，我不知道如何训练多个类.所以，这是我的问题:如何将全套类标签传递给fit函数?
On inspection, the documentation for the fit method says that the truth values are passed as [n_samples, ] -- which matches the error i'm getting -- however, I have no idea then how to train the model with multiple classes. So, this is my question: how do i pass the full set of class labels to the fit function?
我一直无法在Internet上找到用于建模的示例代码，也无法在StackOverflow上找到此问题..但是我感到某些人必须知道该怎么做！
i've been unable to find sample code on the Internet to model, nor this question on StackOverflow.. but i feel certain someone must know how to do it!
在下面的代码中，train_features = [n_samples，nn_features]，true_train = [n_samples，m_classes]
in the code below, train_features = [n_samples, nn_features], truth_train = [n_samples, m_classes]
clf = LogisticRegressionCV(class_weight='balanced', multi_class='multinomial', solver='lbfgs')
clf.fit(train_features, truth_train)
pred = clf.predict(test_features)
推荐答案
您似乎在混淆 multiclass 和 multilabel 这些术语
You seem to be confusing terms multiclass and multilabel http://scikit-learn.org/stable/modules/multiclass.html , in short:
- 多类分类是指具有超过 两节课；例如，对一组水果图像进行分类， 橘子，苹果或梨.多类分类使 假设每个样本都分配给一个且只有一个标签: 水果可以是苹果或梨，但不能同时是两者.
- Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.
因此数据为[n_samples, n_features]，标签为[n_samples]
- 多标签分类为每个样本分配了一组目标 标签.可以将其视为预测数据点的属性 不互斥的主题，例如与 一个文件.文字可能涉及宗教，政治，金融中的任何一个 或同时接受教育或全部都不接受.
- Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.
因此数据为[n_samples, n_features]，标签为[n_samples, n_labels]
您似乎正在寻找multilabel(因为multiclass标签应为1-dim).当前，在sklearn中，唯一支持多标签的方法是:决策树，随机森林，最近邻居，岭回归.
And you seem to be looking for multilabel (as for multiclass labels should be 1-dim). Currently, in sklearn, the only methods supporting multilabel are: Decision Trees, Random Forests, Nearest Neighbors, Ridge Regression.
如果您想了解不同模型的多标签问题，只需将OneVsRestClassifier用作LogisticRegression周围的多标签包装器
If you want to learn multlabel problem with diffent model, simply use OneVsRestClassifier as a multilabel wrapper around your LogisticRegression
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