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

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 <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'

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)?

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

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.

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')