Different feature importance in different algorithms 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.
Feature importance of XGBoost model:

Feature importance of Lasso Regression model:


The short answer is that you likely have multiple variables in the data that measure similar things - so one model picks up one way of measuring that idea (Fibroblasts) and the other picks up a different way (some combination of other variables that are correlated with fibroblasts).
Check for correlations between your predictor variables and consider removing some. I don't know how big your dataset is, but I'm guessing you have too many candidate predictor variables anyway - this type of swing is usually seen in small datasets. There's a "rule of thumb" floating around that you should have 10 events per variable at minimum, but the justification for that rule is weak at best: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0267-3.
