Calculator Options
Formula Used
Error count: Eᵢ = number of errors in category i.
Error rate: Rᵢ = (Eᵢ ÷ Nᵢ) × 100.
Error share: Sᵢ = (Eᵢ ÷ ΣE) × 100.
Weighted score: Wᵢ = Eᵢ × severity weight.
Weighted rate: WRᵢ = Rᵢ × severity weight.
Cumulative percentage: sorted running values divided by displayed values.
How to Use This Calculator
- Enter one category on each data line.
- Separate values using pipes, tabs, semicolons, or commas.
- Provide errors, observations, severity weight, and optional target rate.
- Select the metric, sorting method, orientation, and threshold.
- Choose visual controls and submit the calculator.
- Review the chart, summary cards, and detailed table.
Understanding Error Analysis Charts
Finding Important Error Patterns
Error analysis converts model mistakes into categories that teams can investigate. A bar chart reveals which categories contribute most failures. This view supports faster prioritization than scattered prediction reviews.
Counts show the raw number of mistakes within each category. Rates divide category errors by relevant observation totals. Shares compare each category against all recorded errors.
Adding Business Importance
Weighted scores add business importance to ordinary error counts. Higher weights emphasize costly, risky, or sensitive failures. This approach prevents frequent but harmless errors dominating decisions.
Sorting bars exposes dominant patterns before deeper investigation begins. Descending order supports Pareto analysis and focused debugging. Alphabetical order helps teams locate known categories quickly.
Using Thresholds and Targets
Thresholds separate acceptable performance from urgent investigation areas. Highlighted bars draw attention toward categories crossing limits. Targets also clarify expected performance for every category.
Cumulative percentages show how quickly categories explain overall impact. A steep curve indicates concentrated error sources. A flatter curve suggests many categories require coordinated improvements.
Preparing Reliable Inputs
Reliable inputs need consistent category names and valid denominators. Duplicate labels should be merged before chart generation. Observation totals must represent comparable evaluation populations.
Use separate categories for causes, symptoms, segments, or workflows. Avoid mixing unrelated definitions inside one chart. Clear taxonomy makes every comparison easier to interpret.
Investigating Root Causes
Review the largest bars with representative prediction examples. Check labels, features, preprocessing, thresholds, and class balance. Then connect each finding to a testable corrective action.
Compare charts across model versions using identical evaluation datasets. Stable definitions reveal whether changes truly reduce errors. Shifting datasets can hide regressions or exaggerate improvements.
Interpreting Results Carefully
Error rates deserve context from sample size and uncertainty. Small groups can produce unstable percentages despite few mistakes. Pair charts with confidence intervals during formal evaluations.
This calculator creates an interactive summary for practical model reviews. Exported charts can support reports and team discussions. Repeated analysis builds a disciplined model improvement process.
Checking Segments and Error Types
Segment comparisons uncover failures hidden by aggregate performance. Evaluate important classes, regions, devices, and user groups. Consistent slicing supports safer and fairer deployment decisions.
False positives and false negatives often carry different consequences. Separate them whenever operational costs differ significantly. Balanced summaries can otherwise conceal serious model risks.
Improving Data and Documentation
Data quality problems frequently appear as concentrated error categories. Investigate missing values, label noise, and distribution shifts. Correcting inputs may outperform complicated algorithm changes.
Document category definitions, filters, dates, and model versions carefully. Reproducible settings make later comparisons consistently credible. Shared documentation also reduces disagreement during reviews.
Frequently Asked Questions
1. What does an error analysis bar chart show?
It compares error categories using counts, rates, shares, or weighted scores. Larger bars reveal categories needing closer investigation. Sorting and thresholds help prioritize corrective work.
2. Which input format should I use?
Enter one category per line. Use category, errors, total observations, weight, and target rate. Separate fields with pipes, tabs, semicolons, or commas.
3. When should I use error rate?
Use error rate when category sample sizes differ. It divides errors by category observations. This creates a fairer comparison than raw counts alone.
4. What is error share?
Error share measures each category's portion of all errors. It helps identify categories driving the total failure volume. Shares always depend on the complete submitted dataset.
5. How do severity weights work?
A severity weight multiplies counts or rates. Larger weights increase attention for costly or risky mistakes. Use consistent weights based on documented business consequences.
6. What does the cumulative line represent?
It shows the running percentage of displayed metric values. When bars are descending, it supports Pareto analysis. Steep growth indicates a few dominant error categories.
7. How should I choose an alert threshold?
Choose a threshold linked to operational tolerance or model requirements. Bars meeting that value receive the alert color. The threshold uses the selected metric's units.
8. Can I compare model versions?
Yes, but use identical categories and evaluation populations. Save exported charts for each version. Consistent inputs make trend comparisons more reliable.
9. Why combine excluded categories?
Combining preserves information when showing only top categories. Remaining values become one bar named Other categories. This keeps totals visible without overcrowding the chart.