glavvrach79/breast-cancer-wisconsin

威斯康星州乳腺癌诊断数据集的特征是从乳腺肿块的细针抽吸（FNA）的数字化图像中计算得出的。这些特征描述了图像中细胞核的特性。数据集包含ID号和诊断结果（恶性或良性），以及为每个细胞核计算的十个实值特征，包括半径（从中心到周边点的平均距离）、纹理（灰度值的标准差）、周长、面积、平滑度（半径长度的局部变化）、紧密度（周长^2 /面积 - 1.0）、凹度（轮廓凹部分的严重程度）、凹点（轮廓凹部分的数量）、对称性和分形维度（“海岸线近似” - 1）。

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. The dataset includes ID number, diagnosis (M = malignant, B = benign), and ten real-valued features computed for each cell nucleus: radius (mean of distances from center to points on the perimeter), texture (standard deviation of gray-scale values), perimeter, area, smoothness (local variation in radius lengths), compactness (perimeter^2 / area - 1.0), concavity (severity of concave portions of the contour), concave points (number of concave portions of the contour), symmetry, and fractal dimension ("coastline approximation" - 1).