How to increase the accuracy of 1 class

Question

Hai I am working with blood transfusion data set using SVM classifier.I applied SVC with C=17 and kernel rbf. It is highly imbalanced data set and I balanced it using SMOTE. But class 1 is performing very bad and no prediction in that class. How can I improve this?

from sklearn.svm import SVC
rbf_svc = SVC(C=17)
rbf_svc.fit(X_train,y_train)
y_predict = rbf_svc.predict(X_test)
print('Model accuracy score with rbf kernel and C=17 : {0:0.4f}'. format(accuracy_score(y_test, y_predict)))

Result is:

Model accuracy score with rbf kernel and C=17 : 0.7361

 print(classification_report(y_test,y_predict))

       0       0.74      1.00      0.85       106
       1       0.00      0.00      0.00        38
accuracy                           0.74       144 
macro avg       0.37      0.50      0.42       144
weighted avg       0.54      0.74      0.62       144

          

Answer 1

In class imbalance, the cost of miss classification of dominant class suppresses the cost of miss classification for minority class.

To increase cost of instances of minority class, add class weight in SVC

Class weight can be inversely proportional to count of instances. For eg : if class instances are in 1:10 ratio, class weight will be 10:1

You should relax decision boundary by preventing over fit . Thus lower C will reduce overfitting . Keep it 1 or low.