在上述代码中,find_label_issues函数会返回一个包含标签错误索引的列表。 用户可以根据这些索引进一步分析数据,决定是否修复这些错误。 在识别出标签错误后,用户可以选择修复这些错误。 Cleanlab提供了多种方法来修复标签错误。 使用CleanLearning修复标签 CleanLearning是Cleanlab的一个重要功能,它使用机器学习模型来修复标签...
from cleanlab.classification import CleanLearningissues = CleanLearning(yourFavoriteModel).find_label_issues(data, labels)一行代码，就能衡量和跟踪数据集的整体健康状况：from cleanlab.dataset import overall_label_health_scorehealth = overall_label_health_score(labels, pred_probs)此外，cleanlab 的所有功能都...
复制 from cleanlab.classificationimportCleanLearningissues=CleanLearning(yourFavoriteModel).find_label_issues(data,labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: 代码语言:javascript 复制 from cleanlab.datasetimportoverall_label_health_scorehealth=overall_label_health_score(labels,pred_probs) 此外,clean...
# 加载鸢尾花数据集iris = load_iris()X = pd.DataFrame(iris.data, columns=iris.feature_names)y = pd.Series(iris.target) # 引入Cleanlab并进行标签错误检测clean_learning = CleanLearning()label_issues = find_label_issues(X, y) # 输出标签错误的索引print("标签错误的索引:", label_issues) # ...
from cleanlab.classification import CleanLearningissues = CleanLearning(yourFavoriteModel).find_label_issues(data, labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: from cleanlab.dataset import overall_label_health_scorehealth = overall_label_health_score(labels, pred_probs) ...
find_label_issues(data, labels) 一行代码衡量并跟踪数据集整体健康状况: from cleanlab.dataset import overall_label_health_score # pred_probs = 样本外的预测概率, 通过交叉验证获得dataset_health = overall_label_health_score(labels, pred_probs) 官方公告博客(更多详情):cleanlab.ai/blog/cleanl GitHub:...
find_label_issues(data, labels) # cleanlab trains a robust version of your model that works more reliably with noisy data. cl.fit(data, labels) # cleanlab estimates the predictions you would have gotten if you had trained with *no* label issues. cl.predict(test_data) # A universal data...
fromcleanlab.classificationimportCleanLearningissues = CleanLearning(yourFavoriteModel).find_label_issues(data, labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: fromcleanlab.datasetimportoverall_label_health_scorehealth = overall_label_health_score(labels, pred_probs) 此外,cleanlab 的所有功能都适用于...
(train_data,labels)# 识别错误标签的示例cl.fit(train_data,labels,label_issues=label_issues)preds=cl.predict(test_data)# 从通过自动清理的数据训练过后的模型进行预测fromcleanlab.filterimportfind_label_issuesranked_label_issues=find_label_issues(labels,pred_probs,return_indices_ranked_by="self...
an error. First index is most likely error.fromcleanlab.filterimportfind_label_issuesordered_label_issues=find_label_issues(# One line of code!labels=numpy_array_of_noisy_labels,pred_probs=numpy_array_of_predicted_probabilities,return_indices_ranked_by='normalized_margin',# Orders label issues) ...