print(f'决策树：{mytree}')

这行代码可以打印出生成的决策树模型。`mytree` 是一个 Python 字典类型的变量，存储了整个决策树的信息，包括每个节点的特征、分支和类别。打印出决策树可以帮助我们更好地理解模型的结构，从而更好地进行调试和优化。该代码使用了格式化字符串的方式将决策树的信息输出到控制台。在该字符串中，`{mytree}` 表示将 `mytree` 变量的值插入到字符串中相应的位置。

相关问题

NameError Traceback (most recent call last) <ipython-input-5-66fc4f9977e5> in <module> 213 mytree = createTree(dataSet,labels) 214 print(f'决策树：{mytree}') --> 215 tree_plotter.create_plot(mytree) 216 217 #测试数据 NameError: name 'tree_plotter' is not defined

The error message "NameError: name 'tree_plotter' is not defined" suggests that the name "tree_plotter" was not defined in the code. In other words, the code is trying to call a function or variable named "tree_plotter" but it has not been defined or imported into the current scope.

To fix this error, you may need to check if the module or library containing the "tree_plotter" function has been imported correctly. You can try adding an import statement at the beginning of the code, for example:

import tree_plotter

If the "tree_plotter" function is part of a class or object, you may also need to make sure that you have created an instance of the class or object before calling the function.

Alternatively, if the "tree_plotter" function is not available in any module or library, you may need to define it yourself or find a suitable implementation online.

用python实现决策树

好的，以下是用Python实现决策树的基本步骤：

1. 收集数据：可以使用任何方法收集数据。
2. 准备数据：树构造算法只适用于标称型数据，因此数值型数据必须离散化。
3. 分析数据：可以使用任何方法来分析数据。
4. 训练算法：构造树的数据结构。
5. 测试算法：使用经验树计算错误率。
6. 使用算法：此步骤可以适用于任何监督学习任务，而使用决策树可以更好地理解数据的内在含义。

下面是一个简单的决策树实现的示例代码：

from math import log
import operator

def createDataSet():
    dataSet = [[1, 1, 'yes'],
               [1, 1, 'yes'],
               [1, 0, 'no'],
               [0, 1, 'no'],
               [0, 1, 'no']]
    labels = ['no surfacing', 'flippers']
    return dataSet, labels

def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0
        labelCounts[currentLabel] += 1
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key])/numEntries
        shannonEnt -= prob * log(prob, 2)
    return shannonEnt

def splitDataSet(dataSet, axis, value):
    retDataSet = []
    for featVec in dataSet:
        if featVec[axis] == value:
            reducedFeatVec = featVec[:axis]
            reducedFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reducedFeatVec)
    return retDataSet

def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]
        uniqueVals = set(featList)
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet)/float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
        infoGain = baseEntropy - newEntropy
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

def majorityCnt(classList):
    classCount={}
    for vote in classList:
        if vote not in classCount.keys(): classCount[vote] = 0
        classCount[vote] += 1
    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def createTree(dataSet,labels):
    classList = [example[-1] for example in dataSet]
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)
    return myTree

dataSet, labels = createDataSet()
myTree = createTree(dataSet, labels)
print(myTree)

输出结果为：

{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}

这个决策树表示的是一个针对海洋生物的简单分类器，其中“no surfacing”表示是否浮出水面，“flippers”表示是否有脚蹼。相关问题：

相关问题:
1. 决策树的优缺点是什么？
2. 决策树如何处理连续型数据？
3. 决策树如何处理缺失值？