In a classifier model, we can predict the outcome class, but here I need to find out the features that drive towards a particular result in a classification problem, that are a strong indicator of a particular result eg: driver features for loan default or features indicating for a successful sale and so on. Is it the same as finding the important features for the classifier model, if not, how do I proceed towards this. Also which model could be the most useful in such a case. Thanks in advance.
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.