http://scikit-learn.org/stable/modules/multiclass.html
在实际项目中，我们真的很少用到那些简单的模型，比如LR、kNN、NB等，虽然经典，但在工程中确实不实用。
今天我们关注在工程中用的相对较多的 Multiclass and multilabel algorithms。
warning：scikit-learn的所有分类器都是可以do multiclass classification out-of-the-box（可直接使用），所以没必要使用本节介绍的
sklearn.multiclass module，这里只是讲些知识点。
Below is a summary of the classifiers supported by scikit-learn grouped by strategy; you don’t need the meta-estimators in this class if you’re using one of these unless you want custom multiclass behavior:
- Inherently multiclass: Naive
Bayes, sklearn.lda.LDA, Decision
Trees, Random Forests, Nearest
Neighbors, setting “multi_class=multinomial” in sklearn.linear_model.LogisticRegression.- One-Vs-One: sklearn.svm.SVC.
- One-Vs-All: all linear models except sklearn.svm.SVC.
Some estimators also support multioutput-multiclass classification tasks Decision
Trees, Random Forests, Nearest
Neighbors.
三类问题：
Multiclass classification means
a classification task with more than two classes;但是一个sample只能属于其中一个类别（相当于一个多元分类）。
Multilabel
classification assigns to each sample a set of target labels.一个sample可以属于多个类别（相当于多个二元分类）。
Multioutput-multiclass
classification and multi-task
classification means that a single estimator has to handle several joint classification tasks.（相当于多个多元分类：The
set of labels can be different for each output variable. For instance a sample could be assigned “pear” for an output variable that takes possible values in a finite set of species such as “pear”, “apple”, “orange” and “green” for a second output variable
that takes possible values in a finite set of colors such as “green”, “red”, “orange”, “yellow”...）。
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