Question

我有以下代码来测试一些最流行的sklearn python库的ML算法：

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))

前两个工作正常，但我在LogisticRegression调用中遇到以下错误：

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'

输入数据与之前的调用相同，那么这里发生了什么？

顺便说一句，为什么LinearRegression()和SVR()算法(15.72 vs 3.95)的第一次预测存在巨大差异？

Answer 1

您正在将浮点数传递给分类器，该分类器将分类值作为目标向量。如果将其转换为int，它将被接受作为输入（尽管如果这是正确的方法，那将是有问题的）。

最好使用scikit labelEncoder功能转换您的训练分数。

您的DecisionTree和KNeighbors限定符也是如此。

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

在尝试将浮动数据传递给分类器时，我遇到了同样的问题。我想保留浮点数而不是整数来保证准确性。尝试使用回归算法。例如：

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')

Answer 3

LogisticRegression不是用于回归，而是用于分类！

Y变量必须是分类类，

（例如0或1）

不是continuous变量，

那将是一个回归问题。

LogisticRegression：未知的标签类型：＆＃39;连续＆＃39;在python中使用sklearn

3 个答案: