Question

我有3节课，

A类= 190个样本

B类= 233个样本

C类= 133个样本

我正在使用sklearn多类SVM（svm.svc）来解决这个分类问题但我的测试集将所有内容归类为B类我缺少什么？我尝试改变伽马和C（惩罚参数C），但这没有帮助

import sklearn.metrics as sm
import numpy as np
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn import svm

data = '/Input/AllData.csv'
dataframe = read_csv(data, names=None)

dataframe = dataframe.sample(frac=1).reset_index(drop=True)

dataset = dataframe.values


X = dataset[:,0:-1]
Y = dataset[:,-1]

Count1=0
Count2=0
Count3=0

for i in range(X.shape[0]):
    if(Y[i]==2):
        Count2=Count2+1
    elif(Y[i]==1):
        Count1=Count1+1
    else:
        Count3=Count3+1
print np.isnan(X[:]).any(), np.isnan(Y).any()
print Count1,Count2 ,Count3



X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=0.15, random_state=1)


model =svm.SVC(C=0.01, cache_size=700, class_weight='balanced', coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

model.fit(X_train, Y_train) 
Y_pred = model.predict(X_test)
ACC = sm.accuracy_score(Y_test, Y_pred)
print ACC


target_names = ['A', 'B', 'C']
print(sm.classification_report(Y_test, Y_pred, target_names=target_names))

您可以找到数据here

Answer 1

我使用了这部分代码而 Y_pred 并未将所有内容归类为B类。

您可以查看您的sklearn版本吗？我使用的是0.18。

<强>代码

import sklearn.metrics as sm
import numpy as np
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn import svm

data = 'C:/Users/seral_000/AllData.csv'
dataframe = read_csv(data, names=None)

dataframe = dataframe.sample(frac=1).reset_index(drop=True)
dataset = dataframe.values

X = dataset[:,0:-1]
Y = dataset[:,-1]

X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=0.15, random_state=0)

model = svm.SVC( C                        =   0.01,
                 gamma                    =  'auto',
                 kernel                   =  'linear',
                 degree                   =   3,
                 class_weight             =  'balanced',
                 coef0                    =   0.0,
                 decision_function_shape  =   None,
                 probability              =   False,
                 max_iter                 =  -1,
                 tol                      =   0.001,
                 cache_size               = 700,
                 random_state             =   None,
                 shrinking                =   True,
                 verbose                  =   False
                 )

model.fit(X_train, Y_train) 

Y_pred = model.predict(X_test)
print(Y_pred)

ACC = sm.accuracy_score(Y_test, Y_pred)
print ACC

target_names = ['A', 'B', 'C']
print(sm.classification_report(Y_test, Y_pred, target_names=target_names))

控制台中的结果：

[ 1.  3.  1.  2.  3.  3.  2.  1.  1.  1.  1.  1.  2.  2.  3.  2.  3.  3.
  2.  1.  2.  1.  1.  3.  2.  2.  2.  2.  1.  2.  2.  2.  2.  3.  3.  1.
  3.  2.  2.  1.  2.  3.  1.  1.  2.  2.  3.  2.  1.  3.  1.  2.  1.  1.
  1.  1.  1.  2.  1.  2.  1.  3.  3.  2.  3.  1.  1.  2.  2.  2.  2.  2.
  2.  1.  3.  1.  1.  3.  3.  3.  1.  2.  2.  1.]

0.52380952381

                  precision recall    f1-score    N-supports

          A       0.71      0.56      0.63        39
          B       0.39      0.45      0.42        29
          C       0.45      0.56      0.50        16

avg / total       0.55      0.52      0.53        84

多类SVM将所有样本分类为最可能的claas

1 个答案: