What is the confusion matrix in ML and how is it useful?

Question

Ravi Vishwakarma · Answer

A confusion matrix is a table used to evaluate the performance of a
classification model in machine learning. It shows how well your model’s predictions match the actual labels.
and 
Structure of a Confusion Matrix (for binary classification):
and 
Predicted Positive
Predicted Negative
Actual Positive
True Positive (TP)
False Negative (FN)
Actual Negative
False Positive (FP)
True Negative (TN)
and 
What Each Term Means:
True Positive (TP): Correctly predicted positive.True Negative (TN): Correctly predicted negative.False Positive (FP): Incorrectly predicted positive (aka Type I error).False Negative (FN): Incorrectly predicted negative (aka Type II error).
and 
Why It's Useful:
From the confusion matrix, you can derive many important performance metrics, including:
Accuracy = (TP + TN) / Total
→ Overall, how often is the classifier correct?Precision = TP / (TP + FP)
→ When it predicts positive, how often is it right?Recall (Sensitivity) = TP / (TP + FN)
→ Of all actual positives, how many did it correctly predict?F1 Score = 2 × (Precision × Recall) / (Precision + Recall)
→ Harmonic mean of precision and recall; good for imbalanced classes.
and 
When It’s Most Valuable:
In imbalanced datasets, where accuracy alone is misleading.
To understand types of errors (false positives vs. false negatives), which is crucial in domains like medical diagnosis or fraud detection.

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What is the confusion matrix in ML and how is it useful?

Ravi Vishwakarma

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Structure of a Confusion Matrix (for binary classification):

What Each Term Means:

Why It's Useful:

When It’s Most Valuable:

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	Predicted Positive	Predicted Negative
Actual Positive	True Positive (TP)	False Negative (FN)
Actual Negative	False Positive (FP)	True Negative (TN)