This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks.
How to read it
Use the main plot to read the controlling trend, including break points that a single result card can hide.
Use the sensitivity view to find input combinations where margin collapses quickly.
For early design, focus on which input controls margin before trusting the absolute value.
Learn Confusion Matrix Metrics by dialogue
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When reading Confusion Matrix Metrics, where should I look first? Moving True positive TP changes both the plots and the result cards.
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Start with Precision, but do not treat the number as the whole answer. Use Confusion-matrix heatmap to confirm the assumed state, then read Precision, recall, and F1 for the distribution or trend. Use the main plot to read the controlling trend, including break points that a single result card can hide.
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I can see why True positive TP changes Precision. How should I judge the influence of False positive FP?
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Move False positive FP in small steps and watch Recall. That reveals which term is controlling the result. This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. A single operating point is not enough; sweep the realistic scatter range.
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What is TP-FP-FN sensitivity for? It feels like the ordinary curve already tells the story.
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TP-FP-FN sensitivity is for finding boundaries where the condition becomes risky or margin collapses quickly. Use the sensitivity view to find input combinations where margin collapses quickly. In First-pass comparison of design options before review, the important question is often what happens after a small change, not only the nominal value.
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So if Precision is within the target, can I accept the condition?
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Treat this as a first-pass review. It helps with Narrowing controlling factors and worst-side conditions before detailed analysis and Teaching or explaining the equation, numbers, and visualization under the same inputs, but final decisions still need standards, measured data, detailed analysis, and vendor limits. For early design, focus on which input controls margin before trusting the absolute value.
Practical use
First-pass comparison of design options before review.
Narrowing controlling factors and worst-side conditions before detailed analysis.
Teaching or explaining the equation, numbers, and visualization under the same inputs.
FAQ
Start with Precision and Recall. Then use Confusion-matrix heatmap to confirm the assumed state and Precision, recall, and F1 to read distribution or bias. Use the main plot to read the controlling trend, including break points that a single result card can hide
Move True positive TP alone, then move False positive FP by a comparable amount and compare the change in Precision. TP-FP-FN sensitivity shows combinations where margin or performance changes quickly.
Use it for First-pass comparison of design options before review. Instead of trusting a single point, widen the input range and check whether Precision keeps enough margin before moving to detailed analysis.
This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. Final decisions still require standards, measured data, detailed analysis, and vendor limits.