Accuracy - Revision history

Thakshashila: /* Limitations of Accuracy */

2025-06-10T06:43:21Z

Limitations of Accuracy

← Older revision		Revision as of 06:43, 10 June 2025
Line 34:		Line 34:
	== Limitations of Accuracy ==		== Limitations of Accuracy ==

	Accuracy can be '''misleading''' in cases of [[Imbalanced ~~data~~]].		Accuracy can be '''misleading''' in cases of [[Imbalanced Data]].

	=== Example: Fraud Detection ===		=== Example: Fraud Detection ===

Thakshashila: /* Limitations of Accuracy */

2025-06-10T06:42:46Z

Limitations of Accuracy

← Older revision		Revision as of 06:42, 10 June 2025
Line 34:		Line 34:
	== Limitations of Accuracy ==		== Limitations of Accuracy ==

	Accuracy can be misleading in cases of [[~~imbalanced~~ data]].		Accuracy can be '''misleading''' in cases of [[Imbalanced data]].

	=== Example: Fraud Detection ===		=== Example: Fraud Detection ===

Thakshashila: /* Limitations of Accuracy */

2025-06-10T06:42:06Z

Limitations of Accuracy

← Older revision		Revision as of 06:42, 10 June 2025
Line 34:		Line 34:
	== Limitations of Accuracy ==		== Limitations of Accuracy ==

	Accuracy can be misleading in cases of imbalanced data.		Accuracy can be misleading in cases of [[imbalanced data]].

	=== Example: Fraud Detection ===		=== Example: Fraud Detection ===

Thakshashila: /* SEO Keywords */

2025-06-10T06:19:09Z

SEO Keywords

← Older revision		Revision as of 06:19, 10 June 2025
Line 63:		Line 63:

	accuracy in machine learning, classification accuracy, accuracy formula, model evaluation metric, accuracy vs precision, confusion matrix accuracy, balanced data accuracy		accuracy in machine learning, classification accuracy, accuracy formula, model evaluation metric, accuracy vs precision, confusion matrix accuracy, balanced data accuracy
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Accuracy = '''Accuracy''' is one of the most commonly used metrics to evaluate the performance of a classification model in machine learning. It tells us the proportion of total predictions that were correct. == Definition == : $\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$ Where: * '''TP''' = True Positives * '''TN''' = True Negatives * '''FP''' = False Positives * '''FN''' = False Negatives Accuracy answers the question: '''"Out of all predict..."

2025-06-10T05:20:13Z

Created page with "= Accuracy = '''Accuracy''' is one of the most commonly used metrics to evaluate the performance of a classification model in machine learning. It tells us the proportion of total predictions that were correct. == Definition == :<math> \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} </math> Where: * '''TP''' = True Positives * '''TN''' = True Negatives * '''FP''' = False Positives * '''FN''' = False Negatives Accuracy answers the question: '''"Out of all predict..."

New page

= Accuracy =

'''Accuracy''' is one of the most commonly used metrics to evaluate the performance of a classification model in machine learning. It tells us the proportion of total predictions that were correct.

== Definition ==

:<math> \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} </math>

Where:
* '''TP''' = True Positives
* '''TN''' = True Negatives
* '''FP''' = False Positives
* '''FN''' = False Negatives

Accuracy answers the question: '''"Out of all predictions made by the model, how many were actually correct?"'''

== Simple Example ==

Let’s say a model is used to detect whether emails are spam. Out of 100 emails:

* 60 are correctly identified as spam (TP)
* 30 are correctly identified as not spam (TN)
* 5 are incorrectly marked as spam (FP)
* 5 are incorrectly marked as not spam (FN)

Then:

:<math> \text{Accuracy} = \frac{60 + 30}{60 + 30 + 5 + 5} = \frac{90}{100} = 90\% </math>

== When is Accuracy Useful? ==

Accuracy is useful when the dataset is **balanced** (i.e., both classes occur in roughly equal numbers).

== Limitations of Accuracy ==

Accuracy can be **misleading** in cases of **imbalanced data**.

=== Example: Fraud Detection ===

Imagine 1000 transactions:

* Only 10 are fraudulent.
* A model labels all as “not fraud” and gets 990 correct.

<math> \text{Accuracy} = \frac{990}{1000} = 99\% </math>

Even with 99% accuracy, the model is useless because it failed to detect any fraud.

== Related Metrics ==

* [[Precision]] – Focuses on correct positive predictions
* [[Recall]] – Focuses on correctly identifying actual positives
* [[F1 Score]] – Harmonic mean of Precision and Recall
* [[Confusion Matrix]] – Underlying table for all classification metrics

== Real-World Applications ==

* Image classification (e.g., cat vs dog detection)
* Email spam filters
* Sentiment analysis (positive vs negative review)

== SEO Keywords ==

accuracy in machine learning, classification accuracy, accuracy formula, model evaluation metric, accuracy vs precision, confusion matrix accuracy, balanced data accuracy