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<a class="post-title-link" href="/2017/09/20/kafkaVSFlume/" itemprop="url">kafka和flume的对比</a></h1>
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<p>flume和kafka对比：<br>Kafka、Flume都可以实现数据的传输，但它们的侧重点不同。<br>Kafka追求的是高吞吐量、高负载（topic下可以有多个partition）。<br>kafka是零拷贝，速度快。<br>Flume追求的是数据的多样性：数据来源的多样性、数据流向的多样性。<br>如果数据来源很单一、想要高吞吐的话可以使用Kafka。<br>如果数据来源很多、数据流向很多的话可以使用Flume。<br>也可以将Kafka和Flume结合起来使用。</p>
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<a class="post-title-link" href="/2017/09/19/互信息/" itemprop="url">机器学习--互信息(mutual information）的理解</a></h1>
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<p>再做特征提取是时，那些特征重要，那些不重要呢？怎么确定那个自变量与因变量之间的相关性大小呢？三种方法：</p>
<ol>
<li>逻辑回归中，权重大的特征自然就是相关性大的变量。</li>
<li>person相关系数</li>
<li>互信息</li>
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<a class="post-title-link" href="/2017/09/18/贝叶斯/" itemprop="url">机器学习--朴素贝叶斯分类</a></h1>
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<p>很傻很天真却很很好很强大的贝叶斯定理。。。</p>
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<p>在自然科学、社会科学和工程技术的很多领域中，都不同程度地涉及到对不确定因素和对不完备信息的处理。从实际系统中采集到的数据常常包含着噪声，不够精确甚至不完整。采用纯数学上的假设来消除或回避这种不确定性，效果往往不理想。反之，如果正视它对这些信息进行合适地处理，常常有助于相关实际系统问题的解决。</p>
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