如何在机器学习 model 中使用 test_proportion 数据？

Question

I have a data with 4000 CNN features and it is a binary classification problem. All I know about the test data is the proportions of 1 and 0. How can I tell to my model to predict test labels by using the proportions data? (Like is there a way to say in order to reach this proportions I will give this instance 0.)

How can I use it to increase accuracy? In my case the training data is mostly consist of 1 (85%) and 0(15%) However in my test data proportion of l is given as (%38) So it is much different than training data.

I worked a little bit with balancing the data and it helped. However my model still predicts 1 for nearly all of the data. It may occur because of the adaptation problem also.

As @birdwatch suggested I decrease the threshold for the 0 value and try to increase the 0 label count on the prediction.

# Predicting the Test set results 
y_pred = classifier.predict_proba(X_test) 
threshold=0.3 
y_pred [:,0] = (y_pred [:,0] < threshold).astype('int') 

Before the number of classes were as in follows:

 1 :   8906
 0 :   2968

After changing threshold now it is

1 :  3221
0 :  8653

However is there any other way that I can use test_proportions which ensures the result?

Answer 1

There isn't any sensible way to that. Doing so would create a weird bias in the model. One thing you could do is accept the less likely outcome only is it has high enough score. Normally you'd use 0.5 threshold, but here you might take eg 0.7.