Define ROC in layman terms
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In layman’s terms, the ROC curve visualises the effect of a chosen probability threshold on the classification efficiency. It helps analyse how the efficiency of Binary Classification changes with the values of Probability threshold.
It is used in classification problems.
Can you convert it in more layman terms?
Please give it a try
ROC curve is used in classification problems. The model for classification problems outputs probabilities. ROC curve shows how good the model is at various levels of probabilities. The area under the ROC curve is called AUC and its value ranges from 0 to 1. The closer the AUC value is to 1, better is your model.
it is essentially a graph showing performance of classification model at different thresholds
To Understand ROC first I will try to explain where it is used and why it is used.
As you all know about the classification problems which helps us in identifying the bank fraudulent transactions
and helps in diagnosing diseases.
Many classification algorithms like Logistic Regressor uses probability to distribute samples into classes and in most of the cases we take the threshold value by default 0.5 , which means that the algorithm classifies a sample as positive if the probability of that sample being positive is above 0.5( 50%) and classifies a sample as negative if the probability of that sample being positive is less than 0.5(50%)
This threshold that we have taken may not be best case in case of many situations like in case of detecting a disease it may be wise to choose a lower probability threshold to prevent any chance of the disease going misclassified .Thus the classification of critical data demands a more custom threshold which meets certain requirements. This is where the Receiver operating characteristics comes into picture it illustrates the diagnostic ability of a binary classifier.
In layman’s terms ,the ROC curve visualises the effect of a chosen probability threshold on the classification efficiency. It helps analyse how the efficiency of Binary classification changes with the values of Probability threshold.
A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers. The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.
ROC curve : In Classification Model if the probability of Y as 1 is greater than the probability of Y as 0 then it is known as Concordance and it is represent by ROC curve it shows how good my model is. The value ranges from 0 to 1. The Area under the ROC curve is also known as AUC curve.
1) ROC is a probability curve that plots the TPR against FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve
2) The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes
3)
a) AUC = 1: the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly
b) AUC =0: the classifier would be predicting all Negatives as Positives and all Positives as Negatives
c) 0.5<AUC<1: high chance that the classifier will be able to distinguish the positive class values from the negative class values. This is because the classifier is able to detect more numbers of True positives and True negatives than False negatives and False positives
d) AUC=0.5: the classifier is not able to distinguish between Positive and Negative class points
NOTE:
1) TPR is also called Sensitivity or recall
2) TNR Is also called specificity and 1 – TNR is FPR
3) FPR is 1 – specificity
In layman’s terms, the ROC curve visualises the effect of a chosen probability threshold on the classification efficiency. It helps analyse how the efficiency of Binary Classification changes with the values of Probability threshold.
Receiver Operating Characteristic (ROC) curve is a graphical plot which is generally used for classification problems. It visualises the effect of a chosen probability threshold on the classification efficiency. It helps analyse how the efficiency of Binary classification changes with the values of Probability threshold.
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity/recall.