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 <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)?

Answer 1

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 

Answer 2

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.