Why AUC is not a good metric?

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Why AUC is not a good metric?

This is one of the popular metrics used in the industry. AUC stands for Area Under the Curve. The curve can represents either the ROC curve or the PR curve. In other words, AUC doesn’t care about absolute values, it only cares about ranking.

What is difference between AUC and accuracy?

Accuracy and AUC are two different metrics: Although both are used for measuring the classification performance of a model. To put it simply, accuracy is the measure of the closeness to a specific value. while AUC (Area under the curve) is the measure across all the possible thresholds.

What are the different performance metrics that can be used for multiclass classification problems?

So, this post will be about the 7 most commonly used MC metrics: precision, recall, F1 score, ROC AUC score, Cohen Kappa score, Matthew’s correlation coefficient, and log loss.

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What is the meaning of AUC?

Area under the ROC Curve
AUC stands for “Area under the ROC Curve.” That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1).

Does AUC measure accuracy?

The AUC is an overall summary of diagnostic accuracy. AUC equals 0.5 when the ROC curve corresponds to random chance and 1.0 for perfect accuracy. On rare occasions, the estimated AUC is <0.5, indicating that the test does worse than chance.

Why is AUC used?

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. This is so because the classifier is able to detect more numbers of True positives and True negatives than False negatives and False positives.

What is G mean in machine learning?

The Geometric Mean (G-Mean) is a metric that measures the balance between classification performances on both the majority and minority classes.

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What is the best metric for multiclass classification?

Most commonly used metrics for multi-classes are F1 score, Average Accuracy, Log-loss.

What is the difference between geometric mean and arithmetic mean?

Difference Between Geometric Mean and Arithmetic Mean. Geometric mean of a data set is calculated by taking the nth root of the multiplication of all the numbers in the data set , where ‘n’ is the total number of data points in the set that we considered. Geometric mean is applicable only on a set of positive numbers. For example,…

What is g-mean and how does it work?

G-mean Geometric-mean of the errors, accuracy, F1-scores, etc work in absolute values. You predict a class and you build a confusion matrix. These metrics do not care about probabilities, they only care how many times you said it was positive and it was negative.

How do you find the geometric mean of a data set?

Geometric mean of a data set is calculated by taking the nth root of the multiplication of all the numbers in the data set , where ‘n’ is the total number of data points in the set that we considered. Geometric mean is applicable only on a set of positive numbers.

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What is AUC in logistic regression?

AUC means Area Under the Curve. What curve are you referring to? I will assume it is the ROC curve, which is the most common curve used. ROC AUC ROC AUC is a metric of ranking. Just like spearman correlation or kendall-tau. Example: the true classes are [0,1,1], and your logistic regression gives you probabilities [0.1,0.2,0.2].