LogisticRegression Unknown label type continuous using sklearn in python

Question

I have the following code to test some of most popular ML algorithms of sklearn python library   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     The first two works ok  but I got the following error in LogisticRegression call   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  lt module gt      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    The input data is the same as in the previous calls  so what is going on here    And by the way  why there is a huge diference in the first prediction of LinearRegression   and SVR   algorithms  15 72 vs 3 95

User · Accepted Answer

You are passing floats to a classifier which expects categorical values as the target vector. If you convert it to int it will be accepted as input (although it will be questionable if that's the right way to do it).

It would be better to convert your training scores by using scikit's labelEncoder function.

The same is 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

User · Answer

LogisticRegression is not for regression but classification    The Y variable must be the classification class    for example 0 or 1   And not a continuous variable   that would be a regression problem

User · Answer

I struggled with the same issue when trying to feed floats to the classifiers  I wanted to keep floats and not integers for accuracy  Try using regressor algorithms  For example   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

[python] LogisticRegression: Unknown label type: 'continuous' using sklearn in python

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