Confused by Confusion Matrix? Let’s make it crystal clear!
✅ True Positive: Model got it right—positive was actually positive ❌ False Positive: Predicted positive, but it was actually negative ❌ False Negative: Missed a positive—it said negative ✅ True Negative: Model correctly identified the negative
📊 This simple 2x2 table helps evaluate your classification model’s performance like a pro!
🔁 Save this post. 👨💻 Learn smarter. Build better models.
Data Science School
Confused by Confusion Matrix?
Let’s make it crystal clear!
✅ True Positive: Model got it right—positive was actually positive
❌ False Positive: Predicted positive, but it was actually negative
❌ False Negative: Missed a positive—it said negative
✅ True Negative: Model correctly identified the negative
📊 This simple 2x2 table helps evaluate your classification model’s performance like a pro!
🔁 Save this post.
👨💻 Learn smarter. Build better models.
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3 months ago | [YT] | 0