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简介
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5 years ago
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<li class="chapter " data-level="1.2" data-path="../machine_learning.html">
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<a href="../machine_learning.html">
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机器学习概述
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常见机器学习算法
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<a href="../kNN.html">
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近朱者赤近墨者黑-kNN
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</a>
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<a href="../linear_regression.html">
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最简单的回归算法-线性回归
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</a>
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<li class="chapter " data-level="1.3.3" data-path="../logistic_regression.html">
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<a href="../logistic_regression.html">
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使用回归的思想进行分类-逻辑回归
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</a>
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</li>
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<li class="chapter " data-level="1.3.4" data-path="../decision_tree.html">
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<a href="../decision_tree.html">
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最接近人类思维的算法-决策树
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</a>
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</li>
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<li class="chapter " data-level="1.3.5" data-path="../random_forest.html">
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<a href="../random_forest.html">
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群众的力量是伟大的-随机森林
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</a>
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</li>
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<li class="chapter " data-level="1.3.6" data-path="../kMeans.html">
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<a href="../kMeans.html">
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物以类聚人以群分-kMeans
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</a>
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</li>
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<li class="chapter " data-level="1.3.7" data-path="../AGNES.html">
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<a href="../AGNES.html">
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以距离为尺-AGNES
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.4" data-path="../metrics.html">
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<a href="../metrics.html">
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模型评估指标
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.4.1" data-path="../classification_metrics.html">
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<a href="../classification_metrics.html">
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分类性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.2" data-path="../regression_metrics.html">
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<a href="../regression_metrics.html">
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回归性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.3" data-path="../cluster_metrics.html">
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<a href="../cluster_metrics.html">
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聚类性能评估指标
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.5" data-path="../sklearn.html">
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<a href="../sklearn.html">
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使用sklearn进行机器学习
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</a>
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</li>
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<li class="chapter " data-level="1.6" >
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<span>
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综合实战案例
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</span>
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<ul class="articles">
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<li class="chapter " data-level="1.6.1" >
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5 years ago
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<span>
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泰坦尼克生还预测
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter " data-level="1.6.1.1" data-path="introduction.html">
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5 years ago
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<a href="introduction.html">
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简介
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.2" data-path="EDA.html">
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5 years ago
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<a href="EDA.html">
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探索性数据分析(EDA)
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.3" data-path="feature engerning.html">
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5 years ago
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<a href="feature engerning.html">
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特征工程
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.4" data-path="fit and predict.html">
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5 years ago
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<a href="fit and predict.html">
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构建模型进行预测
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</a>
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</li>
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5 years ago
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<li class="chapter active" data-level="1.6.1.5" data-path="tuning.html">
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5 years ago
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<a href="tuning.html">
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调参
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</a>
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</li>
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</ul>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2" >
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5 years ago
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<span>
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使用强化学习玩乒乓球游戏
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter " data-level="1.6.2.1" data-path="../pingpong/what is reinforce learning.html">
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5 years ago
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<a href="../pingpong/what is reinforce learning.html">
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什么是强化学习
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.2" data-path="../pingpong/Policy Gradient.html">
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5 years ago
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<a href="../pingpong/Policy Gradient.html">
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Policy Gradient原理
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.3" data-path="../pingpong/coding.html">
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5 years ago
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<a href="../pingpong/coding.html">
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使用Policy Gradient玩乒乓球游戏
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</a>
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</li>
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</ul>
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</li>
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5 years ago
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</ul>
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</li>
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<li class="chapter " data-level="1.7" data-path="../recommand.html">
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<a href="../recommand.html">
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实训推荐
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</a>
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</li>
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5 years ago
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<li class="divider"></li>
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<li>
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<h1>
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<a href=".." >调参</a>
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<h1 id="调参">调参</h1>
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<p>很多机器学习算法有很多可以调整的参数(即超参数)，例如我们用的随机森林需要我们指定森林中有多少棵决策树，没棵决策树的最大深度等。这些超参数都或多或少的会影响这模型的性能。那么怎样才能找到合适的超参数，来让我们的模型性能达到比较好的效果呢？可以使用网格搜索!</p>
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<p>网格搜索的意思其实就是遍历所有我们想要尝试的参数组合，看看哪个参数组合的性能最高，那么这组参数组合就是模型的最佳参数。</p>
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<p>sklearn 为我们提供了网格搜索的接口，我们能很方便的进行网格搜索。</p>
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<pre><code class="lang-python"><span class="hljs-keyword">from</span> sklearn.model_selection <span class="hljs-keyword">import</span> GridSearchCV
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<span class="hljs-comment"># 想要调整的参数的字典，字典的key为参数名字，value为想要尝试参数值</span>
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param_grid = {<span class="hljs-string">'n_estimators'</span>: [<span class="hljs-number">10</span>, <span class="hljs-number">20</span>, <span class="hljs-number">50</span>, <span class="hljs-number">100</span>, <span class="hljs-number">150</span>, <span class="hljs-number">200</span>],<span class="hljs-string">'max_depth'</span>: [<span class="hljs-number">5</span>, <span class="hljs-number">10</span>, <span class="hljs-number">15</span>, <span class="hljs-number">20</span>, <span class="hljs-number">25</span>, <span class="hljs-number">30</span>]}
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<span class="hljs-comment"># 采用5折验证的方式进行网格搜索，分类器为随机森林</span>
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grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=<span class="hljs-number">5</span>)
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grid_search.fit(X_train, Y_train)
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<span class="hljs-comment"># 打印最佳参数组合</span>
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print(grid_search.best_params_)
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<span class="hljs-comment"># 打印最佳参数组合时模型的最佳性能</span>
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print(grid_search.best_score_)
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</code></pre>
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<p><img src="../img/58.jpg" alt=""></p>
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<p>可以看到经过调参之后，我们的随机森林模型的性能提高到了 0.8323 ，提升了接近 1% 的准确率。然后我们使用最佳参数构造随机森林，并对测试集测试会发现，测试集的准确率达到了 0.8525。</p>
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<pre><code class="lang-python">Y_train = data[<span class="hljs-string">'Survived'</span>]
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X_train = data.drop([<span class="hljs-string">'Survived'</span>], axis=<span class="hljs-number">1</span>)
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Y_test = test_data[<span class="hljs-string">'Survived'</span>]
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X_test = test_data.drop([<span class="hljs-string">'Survived'</span>], axis=<span class="hljs-number">1</span>)
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clf = RandomForestClassifier(n_estimators=<span class="hljs-number">50</span>, max_depth=<span class="hljs-number">5</span>)
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clf.fit(X_train, Y_train)
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predict = clf.predict(X_test)
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print(accuracy_score(Y_test, predict))
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</code></pre>
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