Calculator inputs
Enter benchmark rows, choose analysis settings, and generate the comparison.
Formula used
The calculator treats lower runtime and higher accuracy as desirable. It normalizes both values before calculating a balanced efficiency score. The selected accuracy weight controls the final ranking.
Pareto models have no faster and more accurate competitor. The normalized area score averages accuracy across the runtime span. Regression predictions remain estimates rather than guaranteed outcomes.
How to use this calculator
- Paste rows containing labels, runtimes, and accuracy values.
- Select the delimiter and correct measurement scales.
- Choose trend, target, weighting, and visualization options.
- Press the generate button to calculate every result.
- Review Pareto points, targets, rankings, and regression metrics.
- Download the chart using its visible toolbar button.
Runtime and Accuracy Insights
Why Both Measures Matter
Runtime and accuracy often compete during model development. Larger models may improve predictions while increasing demand. This plot exposes tradeoffs through clear experimental comparisons.
Runtime measures how long each benchmarked configuration needs. Accuracy measures how often predictions match expected outcomes. Together, these values reveal practical deployment suitability.
Upper-left points are usually attractive. They combine shorter runtimes with stronger predictive performance. However, acceptable positions depend on application requirements.
Understanding Pareto Efficiency
A Pareto point is not dominated by another experiment. No competing point is both faster and more accurate. The frontier highlights configurations deserving closer evaluation.
Linear trends summarize the overall relationship between both metrics. A positive slope suggests accuracy rises with additional runtime. A weak correlation warns against simple conclusions.
Quadratic trends can expose curved performance patterns. They may reveal diminishing gains after longer training periods. Avoid trusting extrapolations beyond the observed runtime range.
Targets and Weighted Decisions
Target lines make operational constraints immediately visible. A runtime target represents latency or training budget limits. An accuracy target represents minimum quality expectations.
Models meeting both targets form the strongest candidate group. Other models may still suit specialized business requirements. Review memory, cost, fairness, and reliability before deployment.
The weighted score combines normalized speed and accuracy values. Higher accuracy weights favor predictive strength over execution speed. Higher runtime weights favor efficient configurations.
Normalized scores help compare metrics using different units. They do not replace direct operational measurements. Always inspect original values beside composite rankings.
Building Reliable Benchmarks
Reliable comparisons require consistent hardware and software environments. Use identical datasets, preprocessing, and evaluation procedures. Repeat benchmarks to reduce random timing variation.
Report warmup behavior when measuring inference runtime. Separate training time from serving latency whenever possible. Document batch sizes because they strongly influence throughput.
Outliers may represent measurement errors or unusual configurations. Investigate them before removing any recorded experiment. Valuable discoveries sometimes appear as unexpected points.
Logarithmic runtime scales improve plots spanning several magnitudes. They compress extreme values without deleting important experiments. Interpret distances carefully after enabling logarithmic scaling.
Selecting Practical Configurations
Moving averages reveal broad patterns across sorted runtime values. They reduce local noise but may hide meaningful differences. Keep the raw points visible for honest interpretation.
A single best model rarely exists for every context. Production systems balance quality, speed, cost, and maintenance. Select configurations aligned with measurable operating constraints.
Use this calculator during experiments and final reviews. Save plots alongside benchmark settings and dataset versions. Consistent evidence supports transparent, repeatable model selection decisions today.
Frequently asked questions
These answers explain common plotting and benchmarking decisions.
1. What does runtime-versus-accuracy show?
It compares computational duration against predictive quality. Each point represents one model or configuration. Upper-left points usually indicate faster and more accurate choices.
2. Which runtime unit should I select?
Select the unit already used by every input row. The calculator does not automatically mix different units. Convert inconsistent measurements before generating the plot.
3. What is a Pareto frontier?
It contains models lacking a strictly better competitor. No other point is both faster and more accurate. Frontier models often deserve detailed operational testing.
4. How does the efficiency score work?
Runtime and accuracy receive normalized values. Accuracy uses your chosen weight percentage. Runtime receives the remaining weight through its speed score.
5. Should I use linear or quadratic trends?
Linear trends suit roughly straight relationships. Quadratic trends can capture curved gains or diminishing returns. Compare fit values and avoid unsupported extrapolation.
6. Why use a logarithmic runtime axis?
Logarithmic scaling helps when runtimes span large magnitudes. It compresses extreme values and reveals crowded smaller values. All runtime entries must remain positive.
7. What does the target prediction mean?
It estimates accuracy at the supplied target runtime. The selected regression model creates this estimate. Treat predictions cautiously beyond observed benchmark ranges.
8. Can memory control bubble sizes?
Yes. Add memory values as the optional fifth column. Select memory within the bubble size setting. Missing memory values receive a neutral fallback size.
9. Can runtime represent training or inference?
Yes. Runtime may represent training, inference, or complete pipeline duration. Keep every experiment measured under the same timing definition. Consistency makes comparisons fair, useful, and easier to explain.