Is it a problem if my model does not perform as well as the stupid model? Usually what I see is that the baseline accuracy and the base model have different accuracies so then the goal is to clean the data and do some feature engineering etc to build a model that performs better than the baseline. Everyone including my professor says that the goal is to build a model than performs better than the stupid model/baseline.
My baseline accuracy is 95.13%. My CART model is also at the exact same performance. In fact, any model I throw at the dataset gives the same accuracy. My target (binary stroke outcome) is highly imbalanced (95% [outcome 0.0] / 5% [outcome 1.0]).
When I perform baseline before feature engineering the accuracy of my CART model and any other model, is 95.13%. After feature engineering they are both still 95.13%.
Is it a coincidence that the target imbalance is also 95%? Not a coincidence right?
Building models for exploration such as KNN, Logistic, C5, CART, NN, they all underperform when comparing with the baseline of 95.13%. Building these models before feature engineering they perform around the same range of 70%-75% and after feature engineering 75%-82%.
Naturally I am performing this baseline analysis without any balancing since the point is to build a stupid model as a benchmark.
So is it ok if my models even after feature engineering, do not perform as well as the benchmark? How would I explain this?

With unbalanced data, where 95% of cases are zeros, you get 95% accuracy by always predicting zeros. It’s likely not a coincidence. If you can’t beat that, your models are rather useless. If you tried different models and are never able to beat the benchmark, maybe you just don’t have relevant data that would allow to make meaningful predictions? Did you explore the predictions? Are you sure that there’s no bugs in reading and preprocessing the data, and that the labels are correct?
On another hand, as others already mentioned, accuracy is a rather poor metric. What exactly you need this model for? Maybe you care more about something like recall and there are differences between the models? Of course, I’m not suggesting here to cherry-pick metrics. Also, if you use a different metric, you should as well compare it to some benchmark.
