决策树的可视化有助于理解模型的决策逻辑。可以使用graphviz库配合scikit-learn的export_graphviz函数绘制决策树图形：from sklearn.tree import export_graphviz import graphviz dot_data = export_graphviz(clf, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names, filled=True, rounded...
tree[val] = node def predict(self, features): if self.root is True: return self.label return self.tree[features[self.feature]].predict(features) class DTree(): def __init__(self, epsilon=0.1): # 信息增益阈值, < epsilon 时,结束决策树展开 self.epsilon = epsilon self._tree = {} @...
A new approach to the problem of graph and subgraph isomorphism detection from an input graph to a database of model graphs is proposed in this paper. It is based on a preprocessing step in which the model graphs are used to create a decision tree. At run time, subgraph isomorphisms are...
dot_data= tree.export_graphviz(regr_2, out_file=None) graph=pydotplus.graph_from_dot_data(dot_data) graph.write_pdf("dt_5.pdf")'''# Plot the results plt.figure() plt.scatter(X, y, c="darkorange", label="data") plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth...
Ranked #1 onGraph Classification on HIV dataset(Accuracy metric) Get a GitHub badge TaskDatasetModelMetric NameMetric ValueGlobal RankResultBenchmark Node ClassificationCiteseerTREE-GAccuracy74.5# 26 Compare Node ClassificationCora: fixed 20 node per classTREE-GAccuracy83.5# 4 ...
A library to create, minimize and optimize binary decision diagrams https://github.com/pubkey/binary-decision-diagrambdd binary-decision-diagrams truth-table robdd binary-decision-tree mtbdd binary-decision-diagram Updated May 29, 2024 TypeScript ...
Constructing a Decision Tree for Graph-Structured Data and its Applications. Fundamenta Informaticae Vol.66, No.1-2, pp. 131-160, (2005).Geamsakul, W., Yoshida, T., Ohara, K., Motoda, H., Yokoi, H., Takabayashi, K.: Constructing a decision tree for graph-structured data and its ...
Learn more about the Microsoft.VisualStudio.Imaging.KnownImageIds.DecisionTree in the Microsoft.VisualStudio.Imaging namespace.
In this paper we propose a new learning architecture that we call unbalanced decision tree (UDT), attempting to improve existing methods based on directed acyclic graph (DAG) and one-versus-all (OVA) approaches to multi-class pattern classification tasks. Several standard techniques, namely one-ve...