如何在orangepython包中设置和使用样本权重？

Question

我对orangepython数据挖掘包还不熟悉。我用的是橙色2.7。在

我的数据集有一个二进制目标（好的和坏的）。好的实例被向下抽样，采样权重为10。如何在Orange的Windows和Linux版本中设置和使用权重进行分类分析？谢谢你的帮助！在

Answer 1

您必须向数据添加一个新的元列，包含实例权重（请参见Meta attributes和Table.add_meta_attribute）。存储meta列的id并用该meta id调用学习者

import Orange
iris = Orange.data.Table("iris")
# Add some weights to the iris dataset
weight = Orange.feature.Continuous("weight")
weight_id = -10
iris.domain.add_meta(weight_id, weight)
iris.add_meta_attribute(weight, 1.0)
for i in range(50, 150):
     iris[i][weight] = 10

# Train a tree classifier on weighted data.
clsf = Orange.classification.tree.TreeLearner(iris, weight_id)

# Evaluate learner performance on weighted data
results = Orange.evaluation.testing.cross_validation(
    [Orange.classification.tree.TreeLearner,
     Orange.classification.bayes.NaiveLearner],
    (iris, weight_id)  # Note how you pass the weight id to testing functions
)
auc = Orange.evaluation.scoring.AUC(results)
ca = Orange.evaluation.scoring.CA(results)