大家好，我是一名Python数据分析师，希望通过分享我在转行过程中的经验，帮助更多的人踏上数据科学之路。为此，我编写了一系列关于Python和机器学习的文章，包括《15天学会Python编程》、《每天10分钟，用Python学数据分析》、《Python数据可视化实战》以及《33天搞定机器学习》。

在上一篇文章中，我们深入探讨了训练集、验证集和测试集的区别，特别是验证集和测试集之间的区别，并介绍了交叉验证的不同方法及其具体用途。本文将继续讨论如何评估模型，并介绍交叉验证在实际操作中的应用。

再谈模型评估

创建一个模型之后，我们需要对其进行评估，以了解其性能如何。不同的模型有不同的评估指标。对于回归模型，常用的评估指标包括均方根误差（RMSE）、平均绝对误差（MAE）、均方误差（MSE）和决定系数（R²）。而对于分类模型，则通常关注准确率、查准率、查全率、F1分数以及ROC曲线和AUC值。

为了确保模型的准确性，我们可以将数据集分为训练集、验证集和测试集。训练集用于训练模型，验证集用于调整模型参数，而测试集则用于检验模型在新数据上的表现。

然而，这种简单的划分方法存在一些问题：

1. 数据集变小：划分出验证集和测试集后，训练集的规模会缩小，可能会影响模型的效果。
2. 模型依赖于数据集的选择：不同的训练集和验证集可能导致模型结果不稳定。
3. 静态划分效率低：简单的划分方式无法高效地进行参数优化和模型选择。

因此，引入交叉验证是一种有效的解决方案。

交叉验证

交叉验证的核心思想是将数据集分成若干部分，轮流使用一部分作为测试集，其余部分作为训练集，从而实现对模型的全面评估。这种方法的优点包括：

1. 减少过拟合：通过交叉验证，可以在一定程度上减少模型在新数据上的过拟合现象。
2. 利用有限数据：可以更有效地利用有限的数据资源。
3. 参数优化：通过多次循环改变参数，可以更准确地找到最优模型。

交叉验证的实现

交叉验证可以通过cross_val_score和cross_validate两个函数来实现。

crossvalscore

cross_val_score用于计算交叉验证的评分情况。其基本语法如下：

python sklearn.model_selection.cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs')

关键参数包括：

• cv：定义交叉验证的策略，可以是整数或自定义的交叉验证迭代器。
• scoring：定义评分标准，可以根据模型类型选择合适的指标。

例如：

```python from sklearn.modelselection import crossval_score from sklearn import svm

clf = svm.SVC(kernel='linear', C=1) scores = crossvalscore(clf, iris.data, iris.target, cv=5) print(scores) ```

运行结果：

[0.96, 1.0, 0.96, 0.96, 1.0]

评分的平均值和95%置信区间：

python print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

cross_validate

cross_validate提供了更强大的功能，不仅可以返回交叉验证的评分，还可以返回训练集评分、每折训练时间、每折评分时间和每个模型对象。其基本语法如下：

python sklearn.model_selection.cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', return_train_score=False, return_estimator=False, error_score=nan)

关键参数包括：

• cv：定义交叉验证的策略。
• scoring：定义评分标准。
• return_train_score：是否返回训练集评分，默认为False。

例如：

```python from sklearn.modelselection import crossvalidate from sklearn import svm

scoring = ['precisionmacro', 'recallmacro'] clf = svm.SVC(kernel='linear', C=1, randomstate=0) scores = crossvalidate(clf, iris.data, iris.target, scoring=scoring, cv=5) sorted(scores.keys()) print(scores['testrecallmacro']) ```

运行结果：

['fit_time', 'score_time', 'test_precision_macro', 'test_recall_macro'] [0.96666667, 1.0, 0.96666667, 0.96666667, 1.0]

crossvalpredict

除了评分之外，我们还可以使用cross_val_predict来获得交叉验证的预测值。这对于后续的模型改进非常重要，因为可以对比预测值和实际值，找到模型预测错误的地方。

其基本语法如下：

```python from sklearn.modelselection import crossval_predict from sklearn import metrics

clf = svm.SVC(kernel='linear', C=1, randomstate=0) predicted = crossvalpredict(clf, iris.data, iris.target, cv=10) print(predicted) print(metrics.accuracyscore(predicted, iris.target)) ```

运行结果：

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