24 Sep Confusion Matrix in Machine Learning
Confusion Matrix measures the performance of machine learning classification algorithms. It includes two dimensions: Actual and Predicted. A detailed breakdown of the model’s predictions to the actual outcomes is displayed by a confusion matrix.
Structure of a confusion matrix
Consider the Confusion Matrix as a 2×2 table for binary classification. Rows show the actual classes, and columns show the predicted classes. The matrix has two rows and two columns:
Table components in Confusion Matrix
A table in the confusion matrix includes the following components:
- True Positives (TP): The model correctly predicts the positive class (actual was true).
- True Negatives (TN): The model correctly predicts the negative class (actual was false).
- False Positives (FP): The model incorrectly predicts the positive class (actual was false).
- False Negatives (FN): The model incorrectly predicts the negative class (actual was true).
Metrics Derived from the Confusion Matrix
Several important metrics can be calculated from the confusion matrix:
- Accuracy: ((TP + TN) / (TP + TN + FP + FN))
It measures the model performance. - Precision: (TP / (TP + FP))
It measures the accuracy of the model’s positive predictions. - Recall (Sensitivity): (TP / (TP + FN))
It identifies relevant instances. - Specificity: (TN / (TN + FP))
It measures the model’s ability to identify negative instances.
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