58 lines
2.3 KiB
YAML
58 lines
2.3 KiB
YAML
classification:
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- name: "accuracy"
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description: "分类正确的样本占总样本数的比例。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "precision"
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description: "预测为正类的样本中，真正类的比例。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "recall"
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description: "真正类样本中被正确预测的比例。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "f1-score"
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description: "精确率和召回率的调和平均值。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "roc_auc"
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description: "ROC 曲线下的面积，衡量模型区分正负样本的能力。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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regression:
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- name: "mean_absolute_error"
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description: "平均绝对误差，表示预测值与真实值之差的绝对值的均值。"
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range: [0, +∞]
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interpretation: "值越小效果越好"
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- name: "mean_squared_error"
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description: "均方误差，表示预测值与真实值之差的平方的均值。"
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range: [0, +∞]
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interpretation: "值越小效果越好"
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- name: "r2_score"
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description: "决定系数，表示模型解释数据方差的能力，1 表示完美拟合。"
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range: [-∞, 1]
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interpretation: "值越大效果越好"
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- name: "explained_variance_score"
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description: "解释方差，衡量预测数据与真实数据的方差相似程度。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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clustering:
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- name: "adjusted_rand_score"
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description: "调整兰德指数，衡量聚类结果与真实标签的相似度。"
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range: [-1, 1]
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interpretation: "值越大效果越好"
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- name: "homogeneity_score"
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description: "同质性得分，衡量聚类的纯度，即每个聚类是否只包含单一类别的样本。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "completeness_score"
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description: "完整性得分，衡量所有同类别样本是否被正确聚类到同一组。"
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range: [0, 1]
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interpretation: "值越大效果越好"
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- name: "silhouette_score"
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description: "轮廓系数，衡量样本在其簇内的紧密度和与其他簇的分离度。"
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range: [-1, 1]
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interpretation: "值越大效果越好"
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