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I am working on a logistic regression binary classification with 1000 rows and 28 columns

While I have split my dataset into train and test, I am using the test data to validate my model. After building the model, I got the below matrxi upon calling .predict()

Please find the confusion matrix below

[[  2  53]
 [  0 190]]

However, my label distribution (in test set) looks like below

status
0     55
1    190

As you can see that most of my labels are 1 but in confusion matrix above most of them are under True Negative. Shouldn't they be either under True Positive, False negative.

It doesn't make sense to see such a huge number under True Negative. because my actual negative is only 55 records as shown above

Am I making any mistake here? Can you guide me here? Am I constructing/interpreting the confusion matrix incorrectly?

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1 Answer 1

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As I see it, your model predicted most units (except 2) into class 1, so this checks out (which is garbage, but that is a different issue).

predicted\true 0 1
0 2 53
1 0 190
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  • $\begingroup$ Is there anyway to get the headers for my confusion matrix? Since, there is no headers, I thought the 1st column is always 1 and 2nd column is 0 $\endgroup$
    – The Great
    Jan 31, 2022 at 12:21
  • $\begingroup$ May I also know what do you mean by so this checks out? I asl because my eng knowledge is limited $\endgroup$
    – The Great
    Jan 31, 2022 at 12:25
  • $\begingroup$ @TheGreat this checks out = this looks good. Anyway, I don't think you can print the labels, at least not easily. I assume you are using sklearn, in which case check scikit-learn.org/stable/modules/generated/…. $\endgroup$ Jan 31, 2022 at 12:26

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