python随机森林分类可视化

在Python中，使用随机森林进行分类的结果可视化可以通过拆解随机森林为单棵树并绘制决策树来实现。首先，导入相关的库和数据集：

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from IPython.display import Image
from sklearn import tree
import pydotplus

# 导入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

然后，通过训练随机森林分类器并获取每棵决策树的列表：

# 训练模型，限制树的最大深度为4
clf = RandomForestClassifier(max_depth=4)
clf.fit(X, y)
Estimators = clf.estimators_

接下来，使用`pydotplus`和`tree.export_graphviz`来将每棵决策树可视化为PDF文件：

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)

以上代码会将每棵决策树可视化为一个PDF文件，文件名格式为"iris_数字.pdf"。你可以通过查看这些PDF文件来观察随机森林的分类结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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