Machine Learning, Exploratory Data Analysis.
importances.plot.bar() Output: XGBoost — Evaluation Counfusion matrix confusion_matrix(y_test, preds) '''OUTPUT array([[1634, 9, 10], [ 6, 259, 0], [ 1, 0, 1381]]) ''' Precision and recall print("Precision:", precision_score(y_test, preds, average='micro')) print("Recall:",recall_score(y_test,