ML之xgboost：利用xgboost算法(自带方式)训练mushroom蘑菇数据集(22+1,6513+1611)来预测蘑菇是否毒性(二分类预测)

输出结果

1、xgboost(num_trees=0): Binary prediction based on  Mushroom Dataset

2、xgboost(num_trees=1): Binary prediction based on  Mushroom Dataset

3、xgboost(num_trees=1,max_depth=4): Binary prediction based on  Mushroom Dataset

设计思路

数据集：Dataset之mushroom：mushroom蘑菇数据集的简介、下载、使用方法之详细攻略

https://blog.csdn.net/qq_41185868/article/details/90480700

核心代码

preds = bst.predict(dtest)

predictions = [round(value) for value in preds]

test_accuracy = accuracy_score(y_test, predictions)

print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))

from matplotlib import pyplot

import graphviz

# num_trees=0

# xgb.plot_tree(bst, num_trees=0, rankdir= 'LR' )  

#xgb.to_graphviz(bst,num_trees=0)

# num_trees=1

xgb.plot_tree(bst,num_trees=1, rankdir= 'LR' )

#xgb.to_graphviz(bst,num_trees=1)