I applied several ML algorithms to my data. My data is made of several predictive numeric features and a target categorical binary feature.
My aim is to build a classifier and predict the target class, based on those predictive features (the target variable is binary). After running multiple algorithms like XGBoost and Lasso regression for example, and after checking feature importance, I get totally different feature importance for each algorithm. What is the meaning of this? look at
Fibroblasts for example, it has 100% importance in one algorithm and zero in the other.