numpy – LogisticRegression: Unknown label type: continuous using sklearn in python

numpy – LogisticRegression: Unknown label type: continuous using sklearn in python

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 thats the right way to do it).

It would be better to convert your training scores by using scikits 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.

numpy – LogisticRegression: Unknown label type: continuous using sklearn in python

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)

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