Machine Learning 12 : Evaluation Metrics
EVALUATION METRICS
The most simple and immediate metric is accuracy
accuracy = labeled correctly / all data
but it depends very much on the number of data in input so with different data is not comparable.
To resolve this we use the confusion matrix
Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa).
Analyzing this data we can extract this two data:
- recall = how many times you get correctly? (similar to accuracy)
true positive / ( true positive + false negative )- precision = once predicted x, what is the probability that is really x?
true positive / ( true positive + false positive )
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