https://terms-en.ai-term-hub.com/zh/tags/ml-basics/Recent content in ML Basics on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/regularization/Sat, 18 Jul 2026 11:32:02 +0000https://terms-en.ai-term-hub.com/zh/terms/regularization/<h2 id="definition">Definition</h2> <p>正则化是机器学习中一个关键概念，旨在不显著增加训练误差的情况下减少泛化误差。它通过阻止模型学习过于复杂的模式来发挥作用。</p> <h3 id="summary">Summary</h3> <p>一组在训练过程中使用的技术，通过对损失函数添加惩罚项或限制模型复杂度来防止过拟合。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>过拟合</li> <li>偏差-方差权衡</li> <li>L1/L2 惩罚</li> <li>丢弃法 (Dropout)</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>深度神经网络训练</li> <li>线性回归模型</li> <li>防止小数据集上的记忆效应</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">from</span> sklearn.linear_model <span style="color:#f92672">import</span> Ridge </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> Ridge(alpha<span style="color:#f92672">=</span><span style="color:#ae81ff">1.0</span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/underfitting-%E6%AC%A0%E6%8B%9F%E5%90%88/">Underfitting (欠拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/cross-validation-%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81/">Cross-validation (交叉验证)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter-tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">Hyperparameter tuning (超参数调优)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/normalization/Sat, 18 Jul 2026 11:28:24 +0000https://terms-en.ai-term-hub.com/zh/terms/normalization/<h2 id="definition">Definition</h2> <p>常见方法包括最小-最大缩放和Z分数标准化。此过程确保具有较大量级的特征不会主导学习算法，特别是在基于梯度的优化中。</p> <h3 id="summary">Summary</h3> <p>归一化是一种数据预处理技术，将数值特征缩放到标准范围（通常为0到1之间），以改善模型的收敛速度和性能。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>最小-最大缩放</li> <li>Z分数标准化</li> <li>特征缩放</li> <li>梯度下降稳定性</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>预处理图像像素值</li> <li>为神经网络准备表格数据</li> <li>提高回归模型的准确性</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">from</span> sklearn.preprocessing <span style="color:#f92672">import</span> MinMaxScaler </span></span><span style="display:flex;"><span><span style="color:#f92672">import</span> numpy <span style="color:#66d9ef">as</span> np </span></span><span style="display:flex;"><span>data <span style="color:#f92672">=</span> np<span style="color:#f92672">.</span>array([[<span style="color:#ae81ff">10</span>], [<span style="color:#ae81ff">20</span>], [<span style="color:#ae81ff">30</span>]]) </span></span><span style="display:flex;"><span>scaler <span style="color:#f92672">=</span> MinMaxScaler() </span></span><span style="display:flex;"><span>normalized_data <span style="color:#f92672">=</span> scaler<span style="color:#f92672">.</span>fit_transform(data) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/standardization-%E6%A0%87%E5%87%86%E5%8C%96/">Standardization (标准化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/data-preprocessing-%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86/">Data Preprocessing (数据预处理)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/feature-engineering-%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B/">Feature Engineering (特征工程)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/generative_model/Sat, 18 Jul 2026 11:18:55 +0000https://terms-en.ai-term-hub.com/zh/terms/generative_model/<h2 id="definition">Definition</h2> <p>生成模型是旨在理解给定数据集中模式和结构的算法，以便创建与原始数据相似的新数据实例。与判别模型不同，生成模型不仅区分数据类别，还学习数据本身的联合概率分布，从而能够“创造”新的数据点，如图像、文本或音频。</p> <h3 id="summary">Summary</h3> <p>一类机器学习模型，通过学习数据的潜在分布来生成与训练数据相似的新颖合成样本。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>概率分布</li> <li>合成数据</li> <li>潜空间</li> <li>训练目标</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>图像生成与编辑</li> <li>自然语言处理</li> <li>小数据集的数据增强</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%88%A4%E5%88%AB%E6%A8%A1%E5%9E%8B-discriminative-model/">判别模型 (Discriminative Model)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B-diffusion-model/">扩散模型 (Diffusion Model)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/transformer%E6%9E%B6%E6%9E%84-transformer/">Transformer架构 (Transformer)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8-autoencoder/">自编码器 (Autoencoder)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/feature/Sat, 18 Jul 2026 11:17:01 +0000https://terms-en.ai-term-hub.com/zh/terms/feature/<h2 id="definition">Definition</h2> <p>在机器学习中，特征是描述数据集中某个实例的独特属性或变量。特征可以是数值型、类别型或文本型，它们构成了机器学习模型的基本输入要素。</p> <h3 id="summary">Summary</h3> <p>被观察现象的单个可测量属性或特征，作为机器学习模型的输入数据。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>输入变量</li> <li>属性</li> <li>数据集列</li> <li>机器学习输入</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>预测建模输入</li> <li>数据分析与可视化</li> <li>特征选择流程</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B/">特征工程</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%A0%87%E7%AD%BE/">标签</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%95%B0%E6%8D%AE%E9%9B%86/">数据集</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%8F%98%E9%87%8F/">变量</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/binary_classification/Sat, 18 Jul 2026 11:09:15 +0000https://terms-en.ai-term-hub.com/zh/terms/binary_classification/<h2 id="definition">Definition</h2> <p>二分类是机器学习中一个基本问题，其输出变量是分类变量，恰好有两个可能的结果，例如真/假或垃圾邮件/非垃圾邮件。常用的算法包括逻辑回归、支持向量机等。</p> <h3 id="summary">Summary</h3> <p>一种监督学习任务，目标是为每个输入实例预测两个可能类别之一。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>决策边界</li> <li>混淆矩阵</li> <li>逻辑回归</li> <li>准确率指标</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>垃圾邮件检测</li> <li>医学诊断</li> <li>欺诈检测</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/multiclass-classification-%E5%A4%9A%E5%88%86%E7%B1%BB/">Multiclass Classification (多分类)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/supervised-learning-%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">Supervised Learning (监督学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/roc-curve-roc%E6%9B%B2%E7%BA%BF/">ROC Curve (ROC曲线)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/precision-%E7%B2%BE%E7%A1%AE%E7%8E%87/">Precision (精确率)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/supervised_learning/Sat, 18 Jul 2026 11:02:04 +0000https://terms-en.ai-term-hub.com/zh/terms/supervised_learning/<h2 id="definition">Definition</h2> <p>在监督学习中，算法在带标签的数据集上进行训练，意味着每个输入示例都与正确的输出配对。目标是让模型学习输入与输出之间的潜在关系。</p> <h3 id="summary">Summary</h3> <p>一种机器学习范式，模型基于带标签的训练示例学习将输入映射到输出。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>带标签数据</li> <li>输入-输出映射</li> <li>分类</li> <li>回归</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>垃圾邮件检测</li> <li>房价预测</li> <li>图像识别</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">from</span> sklearn.linear_model <span style="color:#f92672">import</span> LinearRegression </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> LinearRegression() </span></span><span style="display:flex;"><span>model<span style="color:#f92672">.</span>fit(X_train, y_train) </span></span><span style="display:flex;"><span>predictions <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>predict(X_test) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/">无监督学习</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%AE%AD%E7%BB%83%E9%9B%86/">训练集</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%AA%8C%E8%AF%81%E9%9B%86/">验证集</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%A8%A1%E5%9E%8B%E5%87%86%E7%A1%AE%E7%8E%87/">模型准确率</a></li> </ul>