Metric to evaluate binary classification model on imbalanced dataset in order to meet percentage limitations For a university class I'm working on a imbalanced dataset that has ratio of 43:1 Class_0 to Class_1.
Class_1 refer to companies that have declared bankruptcy based on feature columns of the dataset. On the other hand Class_0 refer to companies that haven't. My goals is to create the best possible classification model that will identify those companies that will go bankrupt but I have the following two limitations:
The model must find with a success rate of at least 62% the companies that will go bankrupt.
The model must find with a success rate of at least 70% the companies that won't go bankrupt.
I've implemented my python code in Colab and I've run several models (Logistic Regression, Naive Bayes, k-Nearest Neighbors, SVM, Neural Networks, etc) with of course tuninig their hyperparameters and I have pass the results of some metrics in a .xlsx file. Of course I have noted down results for the same dataset but after undersampling the majority Class_0 in training set and bringing down the ratio to 3:1.
I asked my professor which metric should I use for meeting the percentage limitations of 62% and 70% I mentioned earlier and he told me that it depends from which class is considered positive and that I should look up here: Confusion Matrix-Wikipedia.
My .xlsx file looks somethink like this:

*

*Unbalanced


*Balanced

Also after Model.fit() I print a classification report to help me understand what my model finds for each class:

My question is what metric should I use from the link for the evaluation he wants?

You need to meet both criteria, and that’s all there is to it. No matter what any single metric says, if you fall short of either the $62\%$ or $70\%$ requirements, your model is inadequate.
Good $F_1$ score but inadequate sensitivity? FAIL
Good $AUC$ but inadequate specificity? FAIL
You are not in a position where you can consider just one metric.
