Eda, Exploratory Data Analysis, Pandas, Python.
xgboost as xgb xgb_fi = feature_importance_sorted(xgb.XGBClassifier(), X_train, y_train) from sklearn.linear_model import LogisticRegression lr = LogisticRegression(max_iter=10000) lr.fit(X_train, y_train.values.ravel()) feature_importances = lr.coef_[0] # Assuming binary classification lr_fi =