1. sklearn中的决策树
2. sklearn基本建模流程
3.决策树的基本流程
4.代码实现
4.1 数据集 —— 红酒
特征值(前13列)目标值(3类)4.2 代码及结果
4.2.1 预测部分
# 获取数据集wine = load_wine()# 划分数据集x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3)# 建模clf = tree.DecisionTreeClassifier(criterion='entropy',random_state=30)clf = clf.fit(x_train, y_train)score = clf.score(x_test, y_test) # 分类的精确度print(score)
结果:
4.2.2 绘制分类树
# 绘制树feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']class_name = ["琴酒","雪莉","贝尔摩德"]dot_data = tree.export_graphviz(clf,feature_names = feature_name,class_names = class_name,filled = True,rounded = True)graph = graphviz.Source(dot_data)graph
结果:
4.2.3 特征重要性
# 特征重要性clf.feature_importances_[*zip(feature_name, clf.feature_importances_)]
结果
5.参数选择
5.1 max_depth 选择
%matplotlib inlineimport matplotlib.pyplot as plttest = []for i in range(10): clf = tree.DecisionTreeClassifier(max_depth = i+1,criterion='entropy',random_state=30)clf = clf.fit(x_train, y_train)score = clf.score(x_test, y_test)test.append(score)plt.plot(range(1,11), test, color='red', label='max_depth')plt.legend()plt.show()
结果:
参考:sklearn菜菜的b站视频以及文档。