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<meta property="og:description" content="k近邻算法（K-Nearest Neighbors）KNN将当前样本的类别归类于距离最近的k个样本的类别 距离公式(2维) 欧式距离$$d = \sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}$$ 曼哈顿距离$$d = |x_1 - x_2| + |y_1 - y_2|$$ 切比雪夫距离$$d = \max\left(|x_1 - x_2|, |y">
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<h4 id="距离公式-2维"><a href="#距离公式-2维" class="headerlink" title="距离公式(2维)"></a><strong>距离公式(2维)</strong></h4><ul>
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<li>欧式距离<br>$$<br>d = \sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}<br>$$</li>
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<li>曼哈顿距离<br>$$<br>d = |x_1 - x_2| + |y_1 - y_2|<br>$$</li>
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<li>切比雪夫距离<br>$$<br>d = \max\left(|x_1 - x_2|, |y_1 - y_2|\right)<br>$$</li>
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</ul>
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<h4 id="k值选择问题"><a href="#k值选择问题" class="headerlink" title="k值选择问题"></a>k值选择问题</h4><table>
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<thead>
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<tr>
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<th>k值</th>
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<th>影响</th>
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</tr>
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</thead>
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<tbody><tr>
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<td>越大</td>
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<td>模型过拟合，准确率波动较大</td>
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</tr>
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<tr>
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<td>越小</td>
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<td>模型欠拟合，准确率趋于稳定但可能较低</td>
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</tr>
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</tbody></table>
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<h3 id="特征预处理"><a href="#特征预处理" class="headerlink" title="特征预处理"></a>特征预处理</h3><blockquote>
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<p>通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 </p>
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</blockquote>
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<ul>
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<li><p>归一化<br>将数据变换到指定区间（默认是[0,1]）<br>$$ x’ = \frac{x- x_{\text {min}}}{x_{\text{max}} - x_{\text{min}}} $$<br>若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \frac{(x - x_{\text{min}}) \cdot (b - a)}{x_{\text{max}} - x_{\text{min}}} $$<br>其中：( [a, b] )：目标区间的范围<br>归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）</p>
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<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> MinMaxScaler <span class="comment"># 归一化</span></span><br></pre></td></tr></table></figure></li>
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<li><p>标准化<br>将数据调整为均值为 0，标准差为 1 的标准正态分布<br>$$ z = \frac{x - \mu}{\sigma} $$<br>( z )：标准化后的值 ( x )：原始数据值 ( $\mu$ )：数据的均值 ( $\sigma$)：数据的标准差</p>
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<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler <span class="comment"># 标准化</span></span><br></pre></td></tr></table></figure></li>
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</ul>
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</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/29139.html">https://rq.shenjianl.cn/posts/29139.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"><a class="post-meta__tags" 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