地球资源数据云——数据资源详情
乳腺癌
该数据集《Breast Cancer》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Breast Cancer Wisconsin (Diagnostic) Data Set 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：breast - cancer - wisconsin - data_data.csv。 Relevant information: Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method - Tree (MSM - T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97 - 101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1 - 4 features and 1 - 3 separating planes. The actual linear program used to obtain the separating plane in the 3 - dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23 - 34].
摘要概览
该数据集《Breast Cancer》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Breast Cancer Wisconsin (Diagnostic) Data Set
任务类型：图像多分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：breast - cancer - wisconsin - data_data.csv。
Relevant information:
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method - Tree (MSM - T) [K. P.
Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97 - 101, 1992], a classification method which uses linear programming to construct a decision tree.
Relevant features were selected using an exhaustive search in the space of 1 - 4 features and 1 - 3 separating planes. The actual linear program used to obtain the separating plane in the 3 - dimensional space is that described in: [K. P. Bennett and O. L.
Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23 - 34].