Home/ Questions/Error: unknown label type: 'continuous' - How to solve it?
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Ambre Parmentier
  • 30
Asked: 2022-05-12T10:17:14+00:00 In:

Error: unknown label type: ‘continuous’ – How to solve it?

  • 30

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This is the program I run:

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))

After I run, it returns an error:

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'

Does anyone have any suggestions for the problem below: unknown label type: ‘continuous’ in the python- How to correct it?

continuous
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2 Answers

  1. Best Answer
    Expert

    The cause: This error happens because the floats are being passed to a classifier that requires the value parse type is the target vector.

    Solution: You can use scikit’s labelEncoder function to change your training scores. It is also true for your DecisionTree and KNeighbors qualifier.

    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
    • 14

  2. LogisticRegression is for classification, not regression!

    The classification class must be specified in the Y variable.

    (0 and 1, for example)

    It is not the continuous variable.

    That would be a recalcitration problem.

    • 17

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