[英]How do I interpret a classification table from a logistic regression model
I completed a logistic regression model and a classification table but I am unsure of how to interpret the results of this table.我完成了一个逻辑回归模型和一个分类表,但我不确定如何解释这个表的结果。
The output is as follows输出如下
Predicted_Value
Actual_Value FALSE TRUE
0 515 37
1 89 109
Classic logistic regression outputs a probability [0-1] for a patient to have the value "1" of your actual observed values when you train the model.经典逻辑回归输出概率 [0-1],当您训练模型时,患者的实际观察值具有值“1”。
In order to get a binary Predicted value, then you need to put a threshold on your outputed vector of probabilities.为了获得二进制预测值,您需要在输出的概率向量上设置阈值。
This is what has been done in order to get the contingency table assessing how accurate your prediction model is when compared with the actual value.这样做是为了获得列联表,评估您的预测模型与实际值相比的准确程度。
Here, you can compute for example Accuracy, Sensitivity, Specificity.在这里,您可以计算例如准确度、灵敏度、特异性。
Accuracy = (109 + 515) / sum(tab) = 83.2% correctly predicted patients
Sensitivity = 109 / (109 + 89) = 55.0% correctly predicted Positive patients
Specificity = 515 / (515 + 37) = 92.3% correctly predicted Negative patients
If you change your cut-off then you will have more or less positively predicted patients ;如果你改变你的截止值,那么你或多或少会有积极预测的患者; this will impact your performance criteria so the choice of cut-off value is yours.这将影响您的绩效标准,因此您可以选择临界值。
Here we don't talk about training and validation sets but these informations are important if you want to know whether your model is robust or not.在这里我们不讨论训练和验证集,但是如果您想知道您的模型是否健壮,这些信息很重要。
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