'Confusion Matrix in Machine Learning: Everything You Need to Know' machinelearning confusionmatrixinml
And the total number of predictions is the sum of counts in all 4 quadrants.Total Predictions=TP + TN + FP + FN
At the outset accuracy may seem like a good metric for evaluation. However, it is not a reliable metric when you have anSuppose you’re designing a model to predict if a person has a particular medical condition that is rare—say affects only 0.5% of the population. So in a population of 1000 people, about 5 people will likely have the disease. You clearly have a class imbalance in this case!indicating that the person doesn’t have that particular medical condition.
In this case, a naive model that predicts the majority class all the time will be 99.5% accurate. However, such a model clearly isn't very helpful.You’re making 1000 predictions. And for all of them, the predicted label is class 0.The accuracy score still works out to 995/1000=0.995 🎯 To sum up, Imbalanced class labels distort accuracy scores. And the model is projected to perform better than what is truly warranted.A medical condition that affects a very small fraction of the total population, a model that predicts the majority class all the time will have an
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