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[英]sklearn.LogisticRegression ValueError: Unknown label type: 'continuous'
[英]LogisticRegression: Unknown label type: 'continuous' using sklearn in python
我有以下代码来测试 sklearn python 库的一些最流行的机器学习算法:
import numpy as np
from sklearn import metrics, svm
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
trainingData = np.array([ [2.3, 4.3, 2.5], [1.3, 5.2, 5.2], [3.3, 2.9, 0.8], [3.1, 4.3, 4.0] ])
trainingScores = np.array( [3.4, 7.5, 4.5, 1.6] )
predictionData = np.array([ [2.5, 2.4, 2.7], [2.7, 3.2, 1.2] ])
clf = LinearRegression()
clf.fit(trainingData, trainingScores)
print("LinearRegression")
print(clf.predict(predictionData))
clf = svm.SVR()
clf.fit(trainingData, trainingScores)
print("SVR")
print(clf.predict(predictionData))
clf = LogisticRegression()
clf.fit(trainingData, trainingScores)
print("LogisticRegression")
print(clf.predict(predictionData))
clf = DecisionTreeClassifier()
clf.fit(trainingData, trainingScores)
print("DecisionTreeClassifier")
print(clf.predict(predictionData))
clf = KNeighborsClassifier()
clf.fit(trainingData, trainingScores)
print("KNeighborsClassifier")
print(clf.predict(predictionData))
clf = LinearDiscriminantAnalysis()
clf.fit(trainingData, trainingScores)
print("LinearDiscriminantAnalysis")
print(clf.predict(predictionData))
clf = GaussianNB()
clf.fit(trainingData, trainingScores)
print("GaussianNB")
print(clf.predict(predictionData))
clf = SVC()
clf.fit(trainingData, trainingScores)
print("SVC")
print(clf.predict(predictionData))
前两个工作正常,但在LogisticRegression
调用中出现以下错误:
root@ubupc1:/home/ouhma# python stack.py
LinearRegression
[ 15.72023529 6.46666667]
SVR
[ 3.95570063 4.23426243]
Traceback (most recent call last):
File "stack.py", line 28, in <module>
clf.fit(trainingData, trainingScores)
File "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/logistic.py", line 1174, in fit
check_classification_targets(y)
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/multiclass.py", line 172, in check_classification_targets
raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous'
输入数据与之前调用中的相同,那么这里发生了什么?
顺便说一下,为什么LinearRegression()
和SVR()
算法的第一次预测会有巨大差异(15.72 vs 3.95)
?
您将浮点数传递给分类器,该分类器将分类值作为目标向量。 如果您将其转换为int
,它将被接受为输入(尽管如果这是正确的做法,那将是有问题的)。
最好使用 scikit 的labelEncoder
函数转换您的训练分数。
您的 DecisionTree 和 KNeighbors 限定符也是如此。
from sklearn import preprocessing
from sklearn import utils
lab_enc = preprocessing.LabelEncoder()
encoded = lab_enc.fit_transform(trainingScores)
>>> array([1, 3, 2, 0], dtype=int64)
print(utils.multiclass.type_of_target(trainingScores))
>>> continuous
print(utils.multiclass.type_of_target(trainingScores.astype('int')))
>>> multiclass
print(utils.multiclass.type_of_target(encoded))
>>> multiclass
在尝试将浮点数提供给分类器时,我遇到了同样的问题。 为了准确起见,我想保留浮点数而不是整数。 尝试使用回归算法。 例如:
import numpy as np
from sklearn import linear_model
from sklearn import svm
classifiers = [
svm.SVR(),
linear_model.SGDRegressor(),
linear_model.BayesianRidge(),
linear_model.LassoLars(),
linear_model.ARDRegression(),
linear_model.PassiveAggressiveRegressor(),
linear_model.TheilSenRegressor(),
linear_model.LinearRegression()]
trainingData = np.array([ [2.3, 4.3, 2.5], [1.3, 5.2, 5.2], [3.3, 2.9, 0.8], [3.1, 4.3, 4.0] ])
trainingScores = np.array( [3.4, 7.5, 4.5, 1.6] )
predictionData = np.array([ [2.5, 2.4, 2.7], [2.7, 3.2, 1.2] ])
for item in classifiers:
print(item)
clf = item
clf.fit(trainingData, trainingScores)
print(clf.predict(predictionData),'\n')
LogisticRegression
不是用于回归而是用于分类!
Y
变量必须是分类类别,
(例如0
或1
)
而不是continuous
变量,
那将是一个回归问题。
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