[{"createTime":1735734952000,"id":1,"img":"hwy_ms_500_252.jpeg","link":"https://activity.huaweicloud.com/cps.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=V1g3MDY4NTY=&utm_medium=cps&utm_campaign=201905","name":"华为云秒杀","status":9,"txt":"华为云38元秒杀","type":1,"updateTime":1735747411000,"userId":3},{"createTime":1736173885000,"id":2,"img":"txy_480_300.png","link":"https://cloud.tencent.com/act/cps/redirect?redirect=1077&cps_key=edb15096bfff75effaaa8c8bb66138bd&from=console","name":"腾讯云秒杀","status":9,"txt":"腾讯云限量秒杀","type":1,"updateTime":1736173885000,"userId":3},{"createTime":1736177492000,"id":3,"img":"aly_251_140.png","link":"https://www.aliyun.com/minisite/goods?userCode=pwp8kmv3","memo":"","name":"阿里云","status":9,"txt":"阿里云2折起","type":1,"updateTime":1736177492000,"userId":3},{"createTime":1735660800000,"id":4,"img":"vultr_560_300.png","link":"https://www.vultr.com/?ref=9603742-8H","name":"Vultr","status":9,"txt":"Vultr送$100","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":5,"img":"jdy_663_320.jpg","link":"https://3.cn/2ay1-e5t","name":"京东云","status":9,"txt":"京东云特惠专区","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":6,"img":"new_ads.png","link":"https://www.iodraw.com/ads","name":"发布广告","status":9,"txt":"发布广告","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":7,"img":"yun_910_50.png","link":"https://activity.huaweicloud.com/discount_area_v5/index.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=aXhpYW95YW5nOA===&utm_medium=cps&utm_campaign=201905","name":"底部","status":9,"txt":"高性能云服务器2折起","type":2,"updateTime":1735660800000,"userId":3}]
随机森林是多棵决策树的组合，使用scikit-learn时没有直接的方法显示随机森林，只能拆解成单棵树来显示。使用随机森林的属性clf.estimators_获取随机森林的决策树列表（
注意，estimators后边有一个下划线 ’ _’ ）
<>代码
from sklearn import datasets from sklearn.ensemble import
RandomForestClassifierfrom IPython.display import Image from sklearn import tree
import pydotplus # 仍然使用自带的iris数据 iris = datasets.load_iris() X = iris.data y =
iris.target # 训练模型，限制树的最大深度4 clf = RandomForestClassifier(max_depth=4) #拟合模型 clf
.fit(X, y) Estimators = clf.estimators_ for index, model in enumerate(Estimators
): filename = 'iris_' + str(index) + '.pdf' dot_data = tree.export_graphviz(
model, out_file=None, feature_names=iris.feature_names, class_names=iris.
target_names, filled=True, rounded=True, special_characters=True) graph =
pydotplus.graph_from_dot_data(dot_data) graph.write_pdf(filename)
我们看一下结果为：
我们生成了100个PDF文件，每个文件为一个决策树：
。。。。。
