機器學習--樸素貝葉斯分類演算法學習筆記
阿新 • • 發佈:2018-12-15
一、基於貝葉斯決策理論的分類方法
優點:在資料較少的情況下仍然有效,可以處理多類別問題。
缺點:對於輸入資料的準備方式較為敏感。
適用資料型別:標稱型資料。
現在假設有一個數據集,它由兩類資料構成。
用p1(c1 | x,y)表示資料點(x,y)屬於類別1的概率,用p2(c2 | x,y)表示資料點(x,y)屬於類別2的概率。
那麼對於一個新的資料點(x,y),可以用下面的規則來判斷它的類別。
- 如果p1(c1 | x,y) > p2(c2 | x,y),則屬於類別1。
- 如果p1(c1 | x,y) < p2(c2 | x,y),則屬於類別2。
這就是樸素貝葉斯理論的核心思想,即選擇具有最高概率的決策。
條件概率的計算使用貝葉斯準則。
二、使用樸素貝葉斯進行文件分類
2.1 準備資料
def loadDataSet(): postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'], ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'], ['stop', 'posting', 'stupid', 'worthless', 'garbage'], ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'], ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']] classVec = [0, 1, 0, 1, 0, 1] return postingList, classVec
def createVocabList(dataSet):
vocabSet = set([])
for document in dataSet:
vocabSet = vocabSet | set(document)
return list(vocabSet)
def setOfWords2Vec(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] = 1 else: print("the word: %s is not in my Vacabulary!" %word) return returnVec
2.2 訓練演算法
def trainNB0(trainMatrix, trainCategory):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory)/float(numTrainDocs)
p0Num = ones(numWords)
p1Num = ones(numWords)
p0Denom = 2.0
p1Denom = 2.0
for i in range(numTrainDocs):
if trainCategory[i] == 1:
p1Num += trainMatrix[i]
p1Denom += sum(trainMatrix[i])
else:
p0Num += trainMatrix[i]
p0Denom += sum(trainMatrix[i])
p1Vect = log(p1Num/p1Denom)
p0Vect = log(p0Num/p0Denom)
return p0Vect, p1Vect, pAbusive
listOPosts, listClasses = loadDataSet()
myVacabList = createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVacabList, postinDoc))
p0V, p1V, pAb = trainNB0(trainMat, listClasses)
2.3 測試演算法
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify * p1Vec) + log(pClass1)
p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
if p1 > p0:
return 1
else:
return 0
def testingNB():
list0Posts, listClasses = loadDataSet()
myVacabList = createVocabList(listOPosts)
trainMat = []
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVacabList, postinDoc))
p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses))
testEntry = ['love', 'my', 'dalmation']
thisDoc = array(setOfWords2Vec(myVacabList, testEntry))
print("{} classified as: {}".format(testEntry, classifyNB(thisDoc, p0V, p1V, pAb)))
testEntry = ['stupid', 'garbage']
thisDoc = array(setOfWords2Vec(myVacabList, testEntry))
print("{} classified as: {}".format(testEntry, classifyNB(thisDoc, p0V, p1V, pAb)))
呼叫
testingNB()
輸出: