Question

我是机器学习的初学者，我正在努力实现我的第一个Naive Bayes，以便更好地理解。所以，我有来自http://archive.ics.uci.edu/ml/datasets/Adult的数据集（美国人口普查数据，类别是'＆lt; = 50k'和'＆gt; 50k'）。

这是我的python代码：

#!/usr/bin/python

import sys
import csv

words_stats = {} # {'word': {'class1': cnt, 'class2': cnt'}}
words_cnt = 0

targets_stats = {} # {'class1': 3234, 'class2': 884} how many words in each class
class_stats = {} # {'class1': 7896, 'class2': 3034} how many lines in each class
items_cnt = 0

def train(dataset, targets):
    global words_stats, words_cnt, targets_stats, items_cnt, class_stats

    num = len(dataset)
    for item in xrange(num):
        class_stats[targets[item]] = class_stats.get(targets[item], 0) + 1

        for i in xrange(len(dataset[item])):
            word = dataset[item][i]
            if not words_stats.has_key(word):
                words_stats[word] = {}

            tgt = targets[item]

            cnt = words_stats[word].get(tgt, 0)
            words_stats[word][tgt] = cnt + 1

            targets_stats[tgt] = targets_stats.get(tgt, 0) + 1
            words_cnt += 1

    items_cnt = num

def classify(doc, tgt_set):
    global words_stats, words_cnt, targets_stats, items_cnt

    probs = {} #the probability itself P(c|W) = P(W|c) * P(c) / P(W)
    pc = {} #probability of the class in document set P(c)
    pwc = {} #probability of the word set in particular class. P(W|c)
    pw = 1 #probability of the word set in documet set

    for word in doc:
        if word not in words_stats:
            continue #dirty, very dirty 
        pw = pw * float(sum(words_stats[word].values())) / words_cnt

    for tgt in tgt_set:
        pc[tgt] = class_stats[tgt] / float(items_cnt)
        for word in doc:
            if word not in words_stats:
                continue #dirty, very dirty
            tgt_wrd_cnt = words_stats[word].get(tgt, 0)
            pwc[tgt] = pwc.get(tgt, 1) * float(tgt_wrd_cnt) / targets_stats[tgt]

        probs[tgt] = (pwc[tgt] * pc[tgt]) / pw

    l = sorted(probs.items(), key = lambda i: i[1], reverse=True)
    print probs
    return l[0][0]

def check_results(dataset, targets):
    num = len(dataset)
    tgt_set = set(targets)
    correct = 0
    incorrect = 0

    for item in xrange(num):
        res = classify(dataset[item], tgt_set)
        if res == targets[item]:
            correct = correct + 1
        else:
            incorrect = incorrect + 1

    print 'correct:', float(correct) / num, ' incorrect:', float(incorrect) / num

def load_data(fil):
    data = []
    tgts = []

    reader = csv.reader(fil)
    for line in reader:
        d = [x.strip() for x in line]
        if '?' in d:
            continue

        if not len(d):
            continue

        data.append(d[:-1])
        tgts.append(d[-1:][0])

    return data, tgts

if __name__ == '__main__':
    if len(sys.argv) < 3:
        print './program train_data.txt test_data.txt'
        sys.exit(1)

    filename = sys.argv[1]
    fil = open(filename, 'r')
    data, tgt = load_data(fil)
    train(data, tgt)

    test_file = open(sys.argv[2], 'r')
    test_data, test_tgt = load_data(test_file)

    check_results(test_data, tgt)

它提供了约61％的正确结果。当我打印概率时，我得到以下内容：

{'<=50K': 0.07371606889800396, '>50K': 15.325378327213354}

但是在正确的分类器的情况下，我希望看到两个概率的总和等于1。起初我认为问题出在浮动下溢并试图以对数进行所有计算，但结果类似。我明白省略一些词会影响准确性，但概率是错误的。

我做错了什么或不明白？

为了您的方便我在这里上传了数据集和python脚本： https://dl.dropboxusercontent.com/u/36180992/adult.tar.gz

感谢您的帮助。

Answer 1

Naive Bayes不直接计算概率，而是计算与每个标签的其他分数相对比的“原始分数”，以便对实例进行分类。该分数可以很容易地转换为[0, 1]

范围内的“概率”

total = sum(probs.itervalues())
for label, score in probs.iteritems():
    probs[label] = score / total

但是，请记住，仍并不代表真正的概率，如answer中所述：

朴素贝叶斯倾向于预测几乎总是非常接近于零或非常接近于1的概率。

朴素贝叶斯分类器中的错误

1 个答案: