目录

说在前面数据处理python code结果

说在前面

操作系统：win10python版本：3.6.3kettle版本：8.3数据集：SodaApriori算法：【数据挖掘】笔记二-决策树

数据处理

原数据格式 目标格式 Kettle转换 【数据挖掘】Kettle去除空记录&添加标记

python code

import numpy as np import scipy as sp from sklearn import tree from sklearn.metrics import precision_recall_curve #决策树的基本操作 from sklearn.metrics import classification_report from sklearn.model_selection import train_test_split #数据读入 data = [] labels = [] with open("file.txt",encoding="utf-8") as ifile: for line in ifile: tokens = line.strip().split(';') bol = 0 if tokens[0] == '雨': bol = 1 data_elem=[bol,float(tokens[1]),float(tokens[2])] #print(data_elem) data.append(data_elem) labels.append(tokens[3].rstrip()) x = np.array(data) labels = np.array(labels) y = np.zeros(labels.shape) #标签转换为浮点数 y[labels=='[0,10)']=0 y[labels=='[10,60)']=1 y[labels=='[60,-)']=2 # 拆分训练数据与测试数据 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.4) # 核心代码：使用信息熵作为划分标准，对决策树进行训练 clf = tree.DecisionTreeClassifier(criterion='entropy') clf.fit(x_train, y_train) # 测试结果 answer = clf.predict(x_test) print(classification_report(y_test, answer))

结果

测试集结果 准确率不是很高，可能算法不好，或者数据之间联系不大，俺也木有办法啊

可视化 可视化需要

pip install graphviz

并且安装 这个

import graphviz import os # 可视化决策树，放在最后 os.environ['PATH'] += os.pathsep + 'D:/Program Files (x86)/Graphviz/bin' dot_data = tree.export_graphviz(clf, out_file=None, feature_names=["PH","elect"], class_names=["[0,10)","[10,60)","[60,-)"], filled=True, rounded=True) graph = graphviz.Source(dot_data) graph.format = 'png' graph.render("water", view=True)