How to find the driver features towards a particular result in Classification problems

If you want to know what features are important to predict some outcome, you speak about feature importance (FI). FI can differ between models, e.g. between a random forest and logistic regression. This is because the models take a different perspective on how to predict some outcome.

A mathematically sound way to assess FI in binary classification would be to run a logistic regression with a lasso or ridge penalty. This is easy and the results (the log odds) can be directly interpreted. Larger positive or negative values indicate a larger positive or negative FI et vice versa.

Here is a good R example: https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html

See „Introduction to Statistical Learning“ Ch. 6.2 for some background and demo: https://www-bcf.usc.edu/~gareth/ISL/

I think that some causal feature selection model would work well for you, like the FCI or GFCI algorithm. There's a good library in R called rcausal that implements these algorithms. There's also a corresponding GUI that has some more information about the algorithms used to determine causality.

I found the answer I was looking for at "https://www.kaggle.com/learn/machine-learning-explainability". Hope others with the same doubt might help from it.