How to get the aggregate of all the confusion matrix in python when Stratified 10 fold cross validation is applied

Question

I am using 10-fold cross-validation and evaluating the model on the basis of accuracy and precision. The confusion matrix is generated 10 times for each model. Can anyone please let me know how can I aggregate the confusion matrix and calculate the accuracy?

Thanks!!

Can you give a sample of what is your input and what you would like see in output? — furkanayd, Dec 03 '19 at 13:02

score 0 · Answer 1 · answered Dec 03 '19 at 13:07

You can use cross_val_predict function as follows and use it result as confusion_matrix() argument.

from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict

y_pred = cross_val_predict(clf, x, y, cv=5)
cm = confusion_matrix(y, y_pred)

score 0 · Answer 2 · answered Dec 03 '19 at 13:14

0

You need to provide a bit more information about the format of the data you have!

If you're using tensorflow, you can refer this source

answered Dec 03 '19 at 13:14

Sameer Pusegaonkar

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How to get the aggregate of all the confusion matrix in python when Stratified 10 fold cross validation is applied

2 Answers2