该数据集可用于进行患者乳腺癌治疗结果预测。该训练数据包含78个患者样本,其中34个样本是来自5年内发展到远处转移的患者(标记为"relapse"),其余... 关键词:Breast Cancer,prediction,relapse,DNA microarray analysis,gene expression 乳腺癌 预测 DNA微阵列分析 基因表达 数据大小:75.53M 数据来源信息:Laura J. van...
Breast Cancer(肯特岗生物医学数据集--乳腺癌)数据摘要：Patients outcome prediction for breast cancer. The training data contains 78 patient samples, 34 of which are from patients who had developed distance metastases within 5 years (labelled as "relapse"), the rest 44 samples are from patients who...
威斯康星州乳腺癌数据集是scikitlearm(skleam)库中-一个常用的内置数据集,用于分类任务。该数据集包含了从乳腺癌患者收集的肿瘤特征的测量值,以及相应的良性(benign)或恶性(malignant)标签。以下是对该数据集的简单介绍: 数据集名称:威斯康星州乳腺癌数据集(BreastCancerWisconsinDataset) 数据集来源:数据集最初由威斯康...
Breast Cancer(肯特岗生物医学数据集--乳腺癌) 数据摘要: Patients outcome prediction for breast cancer. The training data contains 78 patient samples, 34 of which are from patients who had developed distance metastases within 5 years (labelled as relapse), the rest 44 samples are from patients who...
机器学习_Breast Cancer(肯特岗生物医学数据集--乳腺癌)介绍.pdf,Breast Cancer(肯特岗生物医学数据集--乳腺癌) 数据摘要: Patients outcome prediction for breast cancer. The training data contains 78 patient samples, 34 of which are from patients who had develo
数据集的地址为:link 在该页面中,可以进入Data Set Description来查看数据的说明文档,另外一个连接是Data Folder查看数据集的下载地址。 这里我们使用的文件是: breast-cancer-wisconsin.data breast-cancer-wisconsin.names 即: 这两个文件,第一个文件(连接)是我们的数据文件,第二个文件(连接)是数据的说明文档。
from sklearn.datasets import load_breast_cancer from sklearn.linear_model import LogisticRegression #导入初始数据 X, y = load_breast_cancer(return_X_y=True) #数据处理clf = LogisticRegression(solver="liblinear", random_state=0).fit(X, y)#逻辑回归pred = clf.predict_proba(X)[:,...
Breast Cancer Wisconsin (Prognostic) Data Set(威斯康星乳腺癌(预后性症状)数据集)数据摘要：Prognostic Wisconsin Breast Cancer Database 中文关键词：多变量,分类,回归,UCI,威斯康星,乳腺癌,预后性症状,英文关键词：MultiVarite,Classification,Regression,UCI,Wisconsin,Breast Cancer,Prognostic,数据格式：TEXT 数据用途...
sklearn数据集中已经包含该数据,可以直接获取。 cancers=datasets.load_breast_cancer() 清理 数据一共有569组30维。其中两个分类分别为 类型个数 良性benign357 恶性malignant212 从数据中看无空值,有几个属性的最小值为0。 cancers_pd.isnull() cancers_pd.min(axis=0) ...