Simple basic metrics to measure the AI model performance

Simple basic metrics to measure the AI model performance

What is Accuracy, Recall, Precision, TP, FP, TN, TP ?

  1. Simple basic metrics to measure the AI model performance
    1. Terms - TP, TN, FP, FN
      1. True Positive and True Negative
      2. False Positive and False Negative
    2. Metrics
      1. Accuracy
      2. Recall
      3. Precision
    3. References

Simple basic metrics to measure the AI model performance

Today, I am going to explain basic metrics used to measure the AI Model. I wanted to keep it very short blog on this topic, after understanding the metrics hard way1. Since, I am writing this during COVID time, consider an example, where I build an AI Model which classifies a person report as COVID or not.

Terms - TP, TN, FP, FN

True Positive and True Negative

False Positive and False Negative

In order to avoid confusion, read from right-left, for example - FALSE-POSITIVE, meaning model predicted the output as positive but it is not right/false.

Metrics

Accuracy

If it is 100% accurate model, the model predicts actual as actual, something like below. In practical world, if the model is 100% accurate means, it is overfitting the model to data, and it needs to be revised.

Recall

What proportion of actual positives was identified correctly ? 1

Below table got 50% recall, meaning 50% of the time if Model predicts COVID, then 50% of the time it is actual.

Precision

What proportion of positive identifications was actually correct ? 1

In below example, precision is 0%, meaning predicted positive output is not right, the Model says - “person doesn’t have covid, but in reality person got covid”

References


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