Python樹回歸解析是一種機(jī)器學(xué)習(xí)技術(shù)，它可以通過訓(xùn)練數(shù)據(jù)集來建立一個預(yù)測模型，然后使用該模型來預(yù)測未知數(shù)據(jù)的輸出值。樹回歸算法是一種基于樹結(jié)構(gòu)的遞歸分裂技術(shù)，它將數(shù)據(jù)不斷地分成更小的子集，直到滿足某個終止條件為止。

Python樹回歸解析的過程主要包括以下幾個步驟：

1. 讀入數(shù)據(jù)：首先需要從文件或者數(shù)據(jù)庫中讀入數(shù)據(jù)集。

import pandas as pd
data = pd.read_csv('data.csv')

2. 特征選擇：選擇最優(yōu)特征作為當(dāng)前節(jié)點的分裂屬性。

def chooseBestFeatureToSplit(dataSet):
numFeatures = len(dataSet.iloc[0]) - 1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataSet.values]
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

3. 構(gòu)建樹：遞歸地構(gòu)建樹結(jié)構(gòu)，直到滿足終止條件。

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

4. 預(yù)測數(shù)據(jù)：根據(jù)已構(gòu)建好的樹結(jié)構(gòu)，對新數(shù)據(jù)進(jìn)行預(yù)測。

def classify(inputTree, featLabels, testVec):
firstStr = list(inputTree.keys())[0]
secondDict = inputTree[firstStr]
featIndex = featLabels.index(firstStr)
for key in secondDict.keys():
if testVec[featIndex] == key:
if type(secondDict[key]).__name__ == 'dict':
classLabel = classify(secondDict[key], featLabels, testVec)
else:
classLabel = secondDict[key]
return classLabel

通過以上步驟，我們就可以得到一個能夠?qū)ξ粗獢?shù)據(jù)進(jìn)行預(yù)測的模型。在實際應(yīng)用中，我們可以使用交叉驗證等方法來評估模型的預(yù)測性能。