1.输出XGBoost特征的重要性 from matplotlib import pyplot pyplot.bar(range(len(model_XGB.feature_importances_)), model_XGB.feature_importances_) pyplot.show() XGBoost 特征重要性绘图 也可以使用XGBoost内置的特征重要性绘图函数 # plot feature importance using built-in function from xgboo
在XGBoost中提供了三种特征重要性的计算方法: ‘weight’ - the number of times a feature is used to split the data across all trees. ‘gain’ - the average gain of the feature when it is used in trees ‘cover’ - the average coverage of the feature when it is used in trees 简单
show the code: # Plot training deviance def plot_training_deviance(clf, n_estimators, X_test, y_test): # compute test set deviance test_score = np.zeros((n_estimators,), dtype=np.float64) for i, y_pred in enumerate(clf.staged_predict(X_test)): test_s
代码如下所示: # -*- coding: utf-8 -*- #导入需要的包 import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score from xgboost import XGBClassifier from xgboost import
https://stackoverflow.com/questions/35983565/how-is-the-parameter-weight-dmatrix-used-in-the-gradient-boosting-procedure xgboost allows for instance weighting during the construction of the DMatrix, as you noted. This weight is directly tied the inst
一.在代码中标记要显示的各种量 tensorboard各函数的作用和用法请参考:https://www.cnblogs.com/lyc-seu/p/8647792.html import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import os #设置当前工作目录 os.chdir(r'H:\Notepad\Tensorflow') def add_layer(inputs, in_size, ou