---
title: "What evaluation metrics would you use for a classification problem?"  
description: "What evaluation metrics would you use for a classification problem?"  
author: "Harry"  
published: 2024-04-24  
updated: 2024-05-29  
canonical: https://www.mindstick.com/forum/160641/what-evaluation-metrics-would-you-use-for-a-classification-problem  
category: "artificial intelligence"  
tags: ["python-3.4", "python", "artificial intelligence", "ai"]  
reading_time: 2 minutes  

---

# What evaluation metrics would you use for a classification problem?

What [evaluation metrics](https://answers.mindstick.com/qa/116722/what-are-evaluation-metrics-in-ml-models) would you use for a [classification](https://www.mindstick.com/blog/11071/clustering-and-classification-with-mahout) [problem](https://yourviews.mindstick.com/view/81399/tackling-the-problem-of-unemployment-during-corona-pandemic)?

## Replies

### Reply by Bhavesh Badani

When evaluating a classification model, we use several [metrics](https://www.mindstick.com/articles/1507/create-custom-dimension-and-metrics-in-google-analytics-api) to measure its performance. Here are some key ones:

**Accuracy**: The proportion of correctly classified instances (TP + TN) out of the total instances. It’s a common metric but can be misleading when classes are imbalanced.

**Precision (Positive Predictive Value)**: The ratio of TP to the total predicted positives (TP + FP). It measures how many of the predicted positive instances are actually positive.

**Recall (Sensitivity, True Positive Rate)**: The ratio of TP to the total actual positives (TP + FN). It quantifies how well the model captures positive instances.

**F1-Score**: The harmonic mean of precision and recall. It balances precision and recall, especially when classes are imbalanced:

F1= 2⋅Precision⋅Recall​ / Precision+Recall

**Receiver Operating Characteristic (ROC) Curve**: A graphical representation of the model’s performance across different thresholds. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR).

**Area Under the ROC Curve (AUC)**: The area under the ROC curve. AUC ranges from 0.5 (random guessing) to 1 (perfect classifier). Higher AUC indicates better performance.

It depends upon the evaluator to choose the appropriate metric based on the need of the problem or the need of any particular bussiness.


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Original Source: https://www.mindstick.com/forum/160641/what-evaluation-metrics-would-you-use-for-a-classification-problem

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