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ROC Curve Analysis
Data science and statistics both need the ROC analysis curve. It shows the performance of a model or test by looking at the total sensitivity versus its fall-out rate.
This plays a crucial role when it comes to figuring out a model’s viability. However, like a lot of technological leaps, this was created because of war. During WWII they used it to detect enemy aircraft. After that, it moved into several other fields. It has been used to detect the similarities of bird songs, accuracy of tests, response of neurons, and more.
When a machine learning model is run, you will receive inaccurate predictions. Some of the inaccuracy is due to the fact that it needed to be labeled, say, true, but was labeled false. And others need to be false and not true.
What are the odds that the prediction is going to be correct? Since statistics and predictions are just supported guesses, it becomes very important that you are right. With an ROC curve, you are able to see how right the predictions are and using the two parable figure out where to place the threshold.
The threshold is where you choose if the binary classification is false or true, negative, or positive. It will also make what your Y and X variables are. As your parables reach each other, your curve will end up losing the space beneath it. This shows you that the model is less accurate no matter where your threshold is placed. When it comes to modeling most algorithms, the ROC curve is the first test performed. It will detect problems very early by letting you know if your model is accurate.
