ROC-AUC vs Accuracy: Which Metric Is More Important?

# ROC-AUC vs Accuracy: Which Metric Is More Important?

Vikram Singh
Assistant Manager - Content
Updated on Oct 17, 2023 11:48 IST

ROC-AUC and Accuracy are the important metrices that are used to evaluate the machine learning model performance. In this article, we will discuss the difference between the ROC-AUC and Accuracy.

It can be tough to decide which metric to use when measuring the effectiveness of a machine learning algorithm: ROC – AUC or Accuracy? Which one is more important?

While both metrics are essential, ROC AUC is generally seen as a more important measure of how good an algorithm is. This metric considers the trade-offs between precision and recall, while Accuracy only looks at how many predictions are correct.

Before starting the article, let’s glimpse at the Confusion matrix.

Confusion MatrixConfusion Matrix is a Model Evaluation Technique used to check the model’s performance.

where

• TP (True Positive) – Number of times the model correctly identifies true value.
• FP (False Positive) – Number of times the model incorrectly identifies a false value.
• FN (False Negative) – Number of times the model incorrectly identifies a true value.
• TN (True Negative) – Number of times the model correctly identifies a false value.

Check Our Web Story: Confusion Matrix

Also Read: Type -1 vs. Type – 2 Error

## What is Accuracy?

When it comes to evaluating machine learning models, Accuracy is always the primary metric. After all, we want our models to be as accurate as possible, right?

Definition

Accuracy is one of the most common and simplest validation used metrics in machine learning that determine the percentage of correct prediction by any model.

In simple terms, it is the ratio of the Number of True Predictions to the Number of total Samples in the set.

Formula:

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Accuracy score ranges from 0 to 100, where 100 is a perfect score while 0 is the worst score.

Note:

• For Uniformly Distributed Data:

Uniformly distributed Data means that of all the samples we have 50% true and 50% False Data.

In this case, Accuracy is very useful to validate the model.

• For Extremely Imbalanced Distribution:

Extremely imbalanced data means that most data (such as 90%-95%) are either True or False.

Let’s take an example where 95% of the data are false (or negative) while the remaining 5% are true (or positive); in this case Accuracy will lead to the miss-conclusion.

## What is ROC-AUC?

ROC-AUC (or Receiver Operating Characteristic Area Under Curve), is a curve that maps the relationship between the True Positive Rate and False Positive Rate of the model across different cut-off thresholds.

• In the ROC-AUC curve, ROC is a probability curve, and AUC represents the degree or measure of separability.
• The higher the AUC, the better the model.

Must Check: What is ROC curve?

### How to Calculate the ROC curve

The ROC curve is generated by calculating and outlining the TPR and FPR, at various thresholds.

TPR (True Positive Rate/Sensitivity) = TP / TP +FN

FPR (False Positive Rate/Specificity) = FP / FP + TN

The ROC-AUC score ranges from 0.5 – 1, where 1 is the best score, and 0.5 indicates that the model is as good as the base model. From the model, we expect High TPR and Low FPR, i.e., we want a larger area under the curve.

## Which Metric Is More Important?

That’s a tough question because it depends on the specific application. In some cases (for uniformly distributed data), Accuracy may be more important than ROC AUC. But in other cases (for extremely Imbalanced data), ROC AUC may be more important.
Generally, AUC is preferred over Accuracy as it is a much better indicator of model performance. This is because AUC uses the True Positive Rate and False Positive Rate of the model across different cut-off thresholds, and if you are using the Accuracy metric, it is advised to use other metrics as well.

## Key Similarities and Differences between Accuracy and AUC-ROC

• Accuracy is one of the most used and easy to understand while AUC requires good knowledge.
• AUC-ROC performs very well for imbalanced data while for balanced data Accuracy metrics perform very well.
• For imbalanced data, AUC performs very well while Accuracy does not perform well for imbalanced data
• AUC measures the model sensitivity and specificity, while accuracy does not distinguish between these.
• AUC and Accuracy metrics are used for classification models
• Both are implemented using the scikit-learn (sk-learn) package of python

## Conclusion

When measuring a predictive model’s performance, there are two essential metrics: ROC AUC and Accuracy. ROC AUC compares the relation between True Positive Rate and False Positive Rate, while Accuracy is simply the percentage of correct predictions.
This article helps you to understand the difference between ROC-AUC and Accuracy.
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## FAQs

What do you mean by ROC-AUC and Accuracy mean in terms of classification model?

ROC stands for Receiver Operating Characteristic, and AUC represents the Area Under the Curve. ROC-AUC is a performance measurement for the classification problems at various threshold settings. Whereas accuracy is the ratio of the number of correct predictions to the total number of predictions. It essentially quantifies how often the model is correct, regardless of what classes are being distinguished.

How is the ROC curve constructed?

The ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The TPR is also known as sensitivity or recall, while the FPR is equal to 1-specificity.

Can a model with high accuracy have a low ROC-AUC score?

Yes, a model can have high accuracy but a low ROC-AUC score, especially in imbalanced data sets where the model may only predict the majority class well but not distinguish between positive and negative classes.

How is AUC a better metric than accuracy in imbalanced datasets?

AUC is considered a better metric than accuracy in imbalanced datasets because it considers the performance across all possible classification thresholds rather than at a single threshold that determines the accuracy. It gives us a sense of the true separability of classes, irrespective of the imbalance in class distributions.