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	confusion matrix, machine learning metrics, classification model evaluation, precision and recall, accuracy, true positive, false negative, F1-score		confusion matrix, machine learning metrics, classification model evaluation, precision and recall, accuracy, true positive, false negative, F1-score
			[[Category:Artificial Intelligence]]

	== Related Pages ==		== Related Pages ==

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	== Related Pages ==		== Related Pages ==

	* [[Precision and Recall]]		* [[Precision]] and [[Recall]]
	* [[F1 Score]]		* [[F1 Score]]
	* [[Classification]]		* [[Classification]]
	* [[Model Evaluation Metrics]]		* [[Model Evaluation Metrics]]

Thakshashila: Created page with "= Confusion Matrix = '''Confusion Matrix''' is a performance measurement tool used in machine learning, particularly for classification problems. It provides a summary of prediction results on a classification problem by comparing the actual labels with those predicted by the model. == What is a Confusion Matrix? == A confusion matrix is a table that describes the performance of a classification model. It shows how many instances were correctly or incorrectly predicte..."

2025-06-10T05:13:29Z

Created page with "= Confusion Matrix = '''Confusion Matrix''' is a performance measurement tool used in machine learning, particularly for classification problems. It provides a summary of prediction results on a classification problem by comparing the actual labels with those predicted by the model. == What is a Confusion Matrix? == A confusion matrix is a table that describes the performance of a classification model. It shows how many instances were correctly or incorrectly predicte..."

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= Confusion Matrix =

'''Confusion Matrix''' is a performance measurement tool used in machine learning, particularly for classification problems. It provides a summary of prediction results on a classification problem by comparing the actual labels with those predicted by the model.

== What is a Confusion Matrix? ==

A confusion matrix is a table that describes the performance of a classification model. It shows how many instances were correctly or incorrectly predicted.

== Structure of a Confusion Matrix ==

For a binary classification problem, the confusion matrix looks like this:

{| class="wikitable" style="text-align:center"
! Actual \ Predicted
! Positive
! Negative
|-
! Positive
| True Positive (TP)
| False Negative (FN)
|-
! Negative
| False Positive (FP)
| True Negative (TN)
|}

== Terminology ==

* '''True Positive (TP)''': Correctly predicted positives (e.g., predicting a patient has a disease and they actually do)
* '''False Positive (FP)''': Incorrectly predicted positives (Type I Error)
* '''True Negative (TN)''': Correctly predicted negatives
* '''False Negative (FN)''': Incorrectly predicted negatives (Type II Error)

== Simple Example ==

Let’s say a model is predicting if emails are spam:

* TP: Spam email correctly predicted as spam.
* FP: Normal email wrongly predicted as spam.
* TN: Normal email correctly predicted as not spam.
* FN: Spam email wrongly predicted as not spam.

== Mathematical Metrics Using the Confusion Matrix ==

Using TP, TN, FP, and FN, we can calculate several key metrics:

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

'''Precision'''
:<math> \text{Precision} = \frac{TP}{TP + FP} </math>

'''Recall (Sensitivity)'''
:<math> \text{Recall} = \frac{TP}{TP + FN} </math>

'''F1 Score'''
:<math> F1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}} </math>

== Why Use a Confusion Matrix? ==

* It gives a complete picture of how a model is performing.
* It helps in understanding the types of errors made by the classifier.
* Better than just relying on accuracy, especially when dealing with imbalanced datasets.

== Visualization ==

You can visualize a confusion matrix as a heatmap to easily see where the model is performing well or poorly.

== Applications ==

* Medical diagnosis (e.g., predicting cancer)
* Fraud detection
* Spam detection
* Sentiment analysis

== SEO Keywords ==

confusion matrix, machine learning metrics, classification model evaluation, precision and recall, accuracy, true positive, false negative, F1-score

== Related Pages ==

* [[Precision and Recall]]
* [[F1 Score]]
* [[Classification]]
* [[Model Evaluation Metrics]]

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