import xgboost as xgbimport pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as plt X, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = y model = xgb.XGBClassifier()model.fit(X, y) importances = model.featur...
import matplotlib.pyplot as plt from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process from xgboost import XGBClassifier from sklearn.preprocessing import OneHotEncoder, LabelEncoder from sklearn import feature_selection from sklearn import ...
ImportError: cannot import name ‘XGBClassifier‘ 自己命名的.py为xgboost.py这个与xgboost中的库文件命名冲突,所以自己不要以xgboost.py对脚本进行命名。
import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df['y'] = y model = xgb.XGBClassifier model.fit(X, y) importances = model.feature_importances_ i...
网上教程基本都是清一色的使用sklearn版本,此时的XGBClassifier有自带属性feature_importances_,而特征名称可以通过model._Booster.feature_names获取,但是对应原生版本,也就是通过DMatrix构造,通过model.train训练的模型,如何获取feature_importance?而且,二者获取的feature_importance又有 ...