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How do you interpret High precision

Precision measures how many of the positively predicted instances by a model are actually true positive instances. It quantifies the accuracy of positive predictions made by the model.

Mathematically, precision is defined as:

Precision=True PositivesTrue Positives+False Positives

Where:

  • True Positives (TP) are the number of correctly predicted positive instances.
  • False Positives (FP) are the number of incorrectly predicted positive instances.

Precision ranges between 0 and 1, with higher values indicating a higher proportion of true positive predictions among all positive predictions.


  • When precision is high, it means that a model is making positive predictions very selectively, and when it predicts a positive instance, it's usually correct. In other words, the model is cautious about making positive predictions.
  • High precision and a high false-negative rate are often related. 
  • When a model is designed to have very high precision, it tends to be cautious about making positive predictions. 

  • This caution can lead to a situation where the model avoids making many false-positive errors but, in doing so, ends up missing some true positive instances. These missed true positives contribute to a high false-negative rate.

False Negative Rate:

  • The false negative rate measures the proportion of actual positive instances that were incorrectly predicted as negative by the model. It quantifies how many positive instances the model missed.

Mathematically, the false negative rate is defined as:

False Negative Rate=False NegativesFalse Negatives+True Positives

Where:

  • False Negatives (FN) are the number of actual positive instances that were incorrectly predicted as negative.

A high false negative rate means that the model is missing a significant number of actual positive instances. It indicates that the model is not very sensitive to positive instances; it's letting many of them go undetected.


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