sklearn实现XGBoost模型训练,预测并实现可视化

xgboost的原理：

参考链接：xgboost原理介绍

1.准备数据集

这利用的是经典的乳腺癌数据集，568条数据，30种特征。
乳腺癌数据集：breast_cancer.csv

import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier
from xgboost import plot_importance
from xgboost import plot_tree
from sklearn import metrics
#1.训练集和测试集准备
df = pd.read_csv('../data/data/breast_cancer.csv')
 #label
print(df.loc[