# Is AUC 0.75 Good?

## Is AUC 0.75 Good?

As a rule of thumb, an AUC above 0.85 means high classification accuracy, one between 0.75 and 0.85 moderate accuracy, and one less than 0.75 low accuracy (D’ Agostino, Rodgers, & Mauck, 2018).

## What does an AUC of 0.6 mean?

In general, the rule of thumb for interpreting AUC value is: AUC=0.5. No discrimination, e.g., randomly flip a coin. 0.6≥AUC>0.5. Poor discrimination.

## Is 70% AUC good?

AUC is interpreted as the probability that a random person with the disease has a higher test measurement than a random person who is healthy. Based on a rough classifying system, AUC can be interpreted as follows: 90 -100 = excellent, 80 – 90 = good, 70 – 80 = fair, 60 – 70 = poor, 50 – 60 = fail.

## What does AUC of 0.7 mean?

When AUC is 0.7, it means there is a 70% chance that the model will be able to distinguish between positive class and negative class. When AUC is approximately 0, the model is actually reciprocating the classes.

## Is AUC of 0.6 good?

The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.

## Is higher AUC better?

The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

## What is a bad AUC?

The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.

## How do I increase my AUC score?

In order to improve AUC, it is overall to improve the performance of the classifier. Several measures could be taken for experimentation. However, it will depend on the problem and the data to decide which measure will work.

## Is AUC 0.7 good?

In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

## Is AUC score accurate?

AUC is better measure of classifier performance than accuracy because it does not bias on size of test or evaluation data. Accuracy is always biased on size of test data. In most of the cases, we use 20% data as evaluation or test data for our algorithm of total training data.

## How do you read an AUC score?

AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0, one whose predictions are 100% correct has an AUC of 1.0.

## Is AUC 0.6 good?

The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.

## Is AUC of 0.7 good?

AUC can be computed using the trapezoidal rule. In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

## Can AUC be higher than accuracy?

First, as we discussed earlier, even with labelled training and testing examples, most classifiers do produce probability estimations that can rank training/testing examples. As we establish that AUC is a better measure than accuracy, we can choose classifiers with better AUC, thus producing better ranking.

## What is a good F1 score?

1An F1 score is considered perfect when it’s 1 , while the model is a total failure when it’s 0 . Remember: All models are wrong, but some are useful. That is, all models will generate some false negatives, some false positives, and possibly both.

## What does a high AUC mean?

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. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

## How accurate is AUC?

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.