sklearn.metrics模块模型评价函数 |
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sklearn中有3种不同的API用于评估模型的预测质量。 估算器得分方法：估算器有一个评分方法，为它们被设计用来解决的问题提供一个默认的评价标准。计分参数：使用交叉验证的模型评价工具（如model_selection.cross_val_score和model_selection.GridSearchCV）依赖于一个内部评分策略。度量函数：sklearn.metrics模块为特定目的实现了评估预测误差的函数。 模型评价函数示例： from sklearn import metrics # 查看模块的函数 dir(metrics) ### 1.Accuracy score import numpy as np from sklearn.metrics import accuracy_score y_pred = [0, 2, 1, 3] y_true = [0, 1, 2, 3] print(accuracy_score(y_true, y_pred)) print(accuracy_score(y_true, y_pred, normalize=False)) # 正确的预测个数 ### 2.Top-k accuracy score # top_k_accuracy_score函数是accuracy_score的泛化。 # 区别在于，只要真实标签与k个最高预测分数之一相关联，预测就被认为是正确的。 # 准确度_分数是k=1的特殊情况。 import numpy as np from sklearn.metrics import top_k_accuracy_score y_true = np.array([0, 1, 2, 2]) y_score = np.array([[0.5, 0.2, 0.2], [0.3, 0.4, 0.2], [0.2, 0.4, 0.3], [0.7, 0.2, 0.1]]) top_k_accuracy_score(y_true, y_score, k=2) # Not normalizing gives the number of "correctly" classified samples top_k_accuracy_score(y_true, y_score, k=2, normalize=False) ### 3.confusion_matrix from sklearn import datasets from sklearn.svm import LinearSVC from sklearn.model_selection import cross_validate from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay import matplotlib.pyplot as plt y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 0, 2, 2, 0, 2] cm=confusion_matrix(y_true, y_pred) print(confusion_matrix(y_true, y_pred)) print(confusion_matrix(y_true, y_pred,normalize='all')) disp = ConfusionMatrixDisplay(confusion_matrix=cm) disp.plot() plt.show() # 二分类 y_true = [0, 0, 0, 1, 1, 1, 1, 1] y_pred = [0, 1, 0, 1, 0, 1, 0, 1] tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel() print(tn, fp, fn, tp) # A sample toy binary classification dataset X, y = datasets.make_classification(n_classes=2, random_state=0) svm = LinearSVC(random_state=0) def confusion_matrix_scorer(clf, X, y): y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) return {'tn': cm[0, 0], 'fp': cm[0, 1], 'fn': cm[1, 0], 'tp': cm[1, 1]} cv_results = cross_validate(svm, X, y, cv=5, scoring=confusion_matrix_scorer) # Getting the test set true positive scores print(cv_results['test_tp']) # Getting the test set false negative scores print(cv_results['test_fn']) print(cv_results['test_tn']) print(cv_results['test_fp']) ### 4.classification_report from sklearn.metrics import classification_report y_true = [0, 1, 2, 2, 0] y_pred = [0, 0, 2, 1, 0] target_names = ['class 0', 'class 1', 'class 2'] print(classification_report(y_true, y_pred, target_names=target_names)) ### 5. hamming_loss from sklearn.metrics import hamming_loss y_pred = [1, 2, 3, 4] y_true = [2, 2, 3, 4] hamming_loss(y_true, y_pred) ### 6. Precision, recall and F-measures from sklearn import metrics y_pred = [0, 1, 0, 0] y_true = [0, 1, 0, 1] metrics.precision_score(y_true, y_pred) metrics.recall_score(y_true, y_pred) metrics.f1_score(y_true, y_pred) metrics.fbeta_score(y_true, y_pred, beta=0.5) metrics.fbeta_score(y_true, y_pred, beta=1) metrics.fbeta_score(y_true, y_pred, beta=2) metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5) import numpy as np from sklearn.metrics import precision_recall_curve from sklearn.metrics import average_precision_score y_true = np.array([0, 0, 1, 1]) y_scores = np.array([0.1, 0.4, 0.35, 0.8]) precision, recall, threshold = precision_recall_curve(y_true, y_scores) print(precision) print(recall) print(threshold) print( average_precision_score(y_true, y_scores)) ## 多分类 from sklearn import metrics y_true = [0, 1, 2, 0, 1, 2] y_pred = [0, 2, 1, 0, 0, 1] print(metrics.precision_score(y_true, y_pred, average='macro')) print(metrics.recall_score(y_true, y_pred, average='micro')) print(metrics.f1_score(y_true, y_pred, average='weighted')) print(metrics.fbeta_score(y_true, y_pred, average='macro', beta=0.5)) print(metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5, average=None)) ### 7. 回归预测的r2_score # r2_score函数计算决定系数，通常表示为R²。 # 它表示模型中自变量所解释的方差（Y）的比例。它提供了拟合度的指示， # 因此通过解释方差的比例来衡量未见过的样本可能被模型预测的程度。 from sklearn.metrics import r2_score y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] r2_score(y_true, y_pred) ### 8. 回归预测的mean_absolute_error from sklearn.metrics import mean_absolute_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_absolute_error(y_true, y_pred) ### 9. 回归预测的mean_squared_error from sklearn.metrics import mean_squared_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_squared_error(y_true, y_pred) ### 10. 回归预测的mean_squared_log_error from sklearn.metrics import mean_squared_log_error y_true = [3, 5, 2.5, 7] y_pred = [2.5, 5, 4, 8] mean_squared_log_error(y_true, y_pred) ### 11.无监督聚类的Silhouette Coefficient from sklearn import metrics from sklearn import datasets import numpy as np X, y = datasets.load_iris(return_X_y=True) from sklearn.cluster import KMeans kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X) labels = kmeans_model.labels_ metrics.silhouette_score(X, labels, metric='euclidean')参考： https://scikit-learn.org/stable/modules/model_evaluation.html#multilabel-ranking-metrics https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation |
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