使用神经网络多类模块在 Azure ML 中构建流(设置见图片)。
关于 Multiclass 的更多信息:
数据流很简单,80/20的拆分。
当我想了解输出并在可能的情况下将输出转换/计算为概率时,我的问题就出现了。输出如下所示:
我的问题:如果我的模型的评分概率输出为 0.6 并且评分标签 = 1,那么评分标签的模型有多确定?我如何确定实际结果是 1?
Can I safely assume that a scored probabilities of 0.80 = 80% chance of outcome? Or what type of outcomes should I watch out for?
To start with, your are in a binary classification setting, not in a multi-class one (we normally use this term when number of classes > 2).
If scored probabilities output for my model is 0.6 and scored labels = 1, how sure is the model of the scored labels 1?
In practice, the scored probabilities are routinely interpreted as the confidence of the model; so, in this example, we would say that your model has 60% confidence that the particular sample belongs to class 1 (and, complementary, 40% confidence that it belongs to class 0).
And how sure can I be that actual outcome will be a 1?
如果您没有自己计算此类结果的任何替代方法(例如不同的模型),我看不出这个问题与您之前的问题有何不同。
我可以安全地假设 0.80 的评分概率 = 80% 的结果机会吗?
这种说法会让专业统计学家发疯;尽管如此,上面关于置信度的说明应该足以满足您的目的(它们确实对 ML 从业者来说已经足够了)。
我在预测班级或班级概率中的答案?也应该有帮助。
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