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[英]sklearn.LogisticRegression ValueError: Unknown label type: 'continuous'
[英]LogisticRegression: Unknown label type: 'continuous' using sklearn in python
我有以下代碼來測試 sklearn python 庫的一些最流行的機器學習算法:
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))
前兩個工作正常,但在LogisticRegression
調用中出現以下錯誤:
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'
輸入數據與之前調用中的相同,那么這里發生了什么?
順便說一下,為什么LinearRegression()
和SVR()
算法的第一次預測會有巨大差異(15.72 vs 3.95)
?
您將浮點數傳遞給分類器,該分類器將分類值作為目標向量。 如果您將其轉換為int
,它將被接受為輸入(盡管如果這是正確的做法,那將是有問題的)。
最好使用 scikit 的labelEncoder
函數轉換您的訓練分數。
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
在嘗試將浮點數提供給分類器時,我遇到了同樣的問題。 為了准確起見,我想保留浮點數而不是整數。 嘗試使用回歸算法。 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
不是用於回歸而是用於分類!
Y
變量必須是分類類別,
(例如0
或1
)
而不是continuous
變量,
那將是一個回歸問題。
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