的決定函數的形狀是不同的,因爲ovo
訓練爲每個2對類組合而ovr
列車一個分類爲嵌合針對所有其他類的每個類的分類器。
我能找到的最好的例子可以found here on http://scikit-learn.org:
SVC和NuSVC實行「一對一」的方式爲多級(Knerr等人,1990年 )。分類。如果n_class
是 類的數量,則n_class * (n_class - 1)/2
分類器被構造爲 ,並且每個分類器訓練來自兩個類的數據。提供一致的 接口與其它分類器,所述decision_function_shape
選項 允許聚集「一對一」分類 的結果,以形狀的決定功能(N_SAMPLES次,n_classes)
>>> X = [[0], [1], [2], [3]]
>>> Y = [0, 1, 2, 3]
>>> clf = svm.SVC(decision_function_shape='ovo')
>>> clf.fit(X, Y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovo', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> dec = clf.decision_function([[1]])
>>> dec.shape[1] # 4 classes: 4*3/2 = 6
6
>>> clf.decision_function_shape = "ovr"
>>> dec = clf.decision_function([[1]])
>>> dec.shape[1] # 4 classes
4
簡單來說這是什麼意思?
要理解n_class * (n_class - 1)/2
意味着什麼,請使用itertools.combinations
生成兩級組合。
def ovo_classifiers(classes):
import itertools
n_class = len(classes)
n = n_class * (n_class - 1)/2
combos = itertools.combinations(classes, 2)
return (n, list(combos))
>>> ovo_classifiers(['a', 'b', 'c'])
(3.0, [('a', 'b'), ('a', 'c'), ('b', 'c')])
>>> ovo_classifiers(['a', 'b', 'c', 'd'])
(6.0, [('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd'), ('c', 'd')])
哪個估計器應該用於多標籤分類?
在你的情況,你有一個問題與多個標籤(就像這裏在StackOverflow)。如果你知道你在提前標籤(班),我可能會建議OneVsRestClassifier(LinearSVC())
但你可以嘗試DecisionTreeClassifier或RandomForestClassifier(我認爲):
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.svm import SVC, LinearSVC
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier
df = pd.DataFrame({
'Tags': [['python', 'pandas'], ['c#', '.net'], ['ruby'],
['python'], ['c#'], ['sklearn', 'python']],
'Questions': ['This is a post about python and pandas is great.',
'This is a c# post and i hate .net',
'What is ruby on rails?', 'who else loves python',
'where to learn c#', 'sklearn is a python package for machine learning']},
columns=['Questions', 'Tags'])
X = df['Questions']
mlb = MultiLabelBinarizer()
y = mlb.fit_transform(df['Tags'].values)
pipeline = Pipeline([
('vect', CountVectorizer(token_pattern='|'.join(mlb.classes_))),
('linear_svc', OneVsRestClassifier(LinearSVC()))
])
pipeline.fit(X, y)
final = pd.DataFrame(pipeline.predict(X), index=X, columns=mlb.classes_)
def predict(text):
return pd.DataFrame(pipeline.predict(text), index=text, columns=mlb.classes_)
test = ['is python better than c#', 'should i learn c#',
'should i learn sklearn or tensorflow',
'ruby or c# i am a dinosaur',
'is .net still relevant']
print(predict(test))
輸出:
.net c# pandas python ruby sklearn
is python better than c# 0 1 0 1 0 0
should i learn c# 0 1 0 0 0 0
should i learn sklearn or tensorflow 0 0 0 0 0 1
ruby or c# i am a dinosaur 0 1 0 0 1 0
is .net still relevant 1 0 0 0 0 0
你可以嘗試都和檢查哪些一個給你的具體數據更好的結果。 –