I am working on a customer segmentation using 5 features such as recency, frequency, monetary, tenure, unique_product_cnt etc.
So, I did a RFM based segmentation where I used
jenks optimization to find clusters/groups of each of the variables.
Later, based on some if-else condition, I created labels (segment names) for these customers. I have a dataset of 2500 records where 1100 belongs to label
1400 belong to label
Now, I am trying to find out what are the features impact/contribution of features to the output label (cluster). like ordering features based on their importance.
So, using the label that I derived, I built a supervised learning model using random forests.
I am building random forests only to understand the feature importance and find out which features were more important in driving the outcome/class label.
So, I did a train, test split and gridsearch over best parameters.
Unfortunately, I see that I get 100% accuracy, f1, recall etc in both
While I don't intend to build a supervised model but my requirement is just to understand what features/characteristics contributes a customer to be either
At Risk. Basically, to understand my cluster formation, I built a supervised model.
So, for this purpose is it okay to live with 100% performance across all metrics?
Or is it cause of concern? Am I making any mistake?
You can find my
f1-score threshold optimization below