使用python中的SVM进行机器学习的分类报告测试集错误

Question

I split the data into test and train sets both of which have the target values '0's and '1's. But after fitting and predicting with SVM the classification report states that there are Zero '0's in the test sample which is not true.

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
df = pd.DataFrame(data = data['data'],columns=data['feature_names'])
x = df
y = data['target']
xtrain,xtest,ytrain,ytest 
= train_test_split(x,y,test_size=0.3,random_state=42)

As you can see below, the test has 0s and 1s but the support in the classification report states that there aren't any 0s!

!( https://i.imgur.com/wjEjIvX.png )

Answer 1

(It is always a good idea to include your relevant code in the example, and not in images)

the classification report states that there are Zero '0's in the test sample which is not true.

This is because, from your code in the linked image, it turns out that you have switched the arguments in the classification_report ; you have used:

print(classification_report(pred, ytest)) # wrong order of arguments

which indeed gives:

             precision    recall  f1-score   support

    class 0       0.00      0.00      0.00         0
    class 1       1.00      0.63      0.77       171

avg / total       1.00      0.63      0.77       171

but the correct usage (see the docs ) is

print(classification_report(ytest, pred)) # ytest first

which gives

             precision    recall  f1-score   support

    class 0       0.00      0.00      0.00        63
    class 1       0.63      1.00      0.77       108

avg / total       0.40      0.63      0.49       171

along with the following warning message:

C:\\Users\\Root\\Anaconda3\\envs\\tensorflow1\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)

because, as already pointed out in the comments, you predict only 1's:

pred
# result:
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

the reason of which is another story, and not part of the current question.

Here is the complete reproducible code for the above:

from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)
xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=0.3,random_state=42)

from sklearn.svm import SVC
svc=SVC()
svc.fit(xtrain, ytrain)
pred = svc.predict(xtest)

print(classification_report(ytest, pred))