In most ML blogs and tutorials, benchmarks look like this:
Tune forever → squeeze the last 0.1% → publish the best number.
That’s useful for competitions.
But it’s not how most production systems are built.
This article takes a different approach.
Benchmarking With Defaults - On Purpose
All SmartKNN benchmarks are evaluated using SmartML, with:
- Default model configurations
- No hyperparameter tuning
- Single run per model
- Identical preprocessing and evaluation protocol
Why?
Because defaults matter.
If you’ve ever deployed a model in the real world, you know:
- Tuning takes time,
- Tuning costs money,
- And tuning is often skipped in early or constrained deployments.
These benchmarks are designed to answer a simple question:
“How do models behave out of the box?”
What These Benchmarks Are (and Aren’t)
What they show
- Relative model behavior under production-like defaults
- Trade-offs between accuracy, latency, and throughput
- Which models are robust without tuning
- Where models break down at scale
What they are not
- Leaderboard-optimized results
- Dataset-specific tuning showcases
- Claims that one model is “universally best”
Yes — performance can improve with tuning.
That’s expected.
But these benchmarks intentionally start from zero tuning.
Why Some Models Underperform
You may notice that:
- Some models perform poorly on certain datasets
- Some models are excluded from large datasets
- Some results look “too good” or “too bad”
- All of this is intentional and transparent.
Reasons include:
- Scaling limitations (e.g. KNN-style models on large datasets)
- Strongly linear or near-deterministic datasets
- Models that require tuning to shine
- Dataset characteristics that don’t match a model’s assumptions
- This reflects real-world behavior, not cherry-picked scenarios.
SmartKNN in Context
SmartKNN is evaluated under the same constraints as every other model:
- No special treatment
- No tuning advantages
- Same SmartML pipeline
In several datasets, SmartKNN shows:
- Very low single-sample latency
- Competitive accuracy on structured and local-pattern datasets
- Trade-offs in batch throughput on very large datasets
That’s exactly the point of these benchmarks:
to surface strengths and weaknesses, not hide them.
Fair, Reproducible, and Open
All benchmarks are evaluated using SmartML, which enforces:
- Consistent preprocessing
- Identical evaluation logic
- No leakage through custom pipelines
- Comparable latency and throughput measurement
This makes the results:
- Fair
- Clear
- Reproducible
Contribute Your Results
You’re not limited to the published benchmarks.
You can:
- Run your own models using SmartML
- Evaluate them under the same default setup
- Submit your results to be displayed on the SmartEco website
This helps build a community-driven, transparent benchmark ecosystem.
View current results here:
SmartEco
Want to Collaborate?
SmartEco is an open ML ecosystem focused on:
- Practical benchmarking
- Production-minded evaluation
- Honest model comparison
- Systems thinking over leaderboard chasing
If you’re interested in:
- contributing benchmarks
- improving SmartML
- experimenting with SmartKNN
- or collaborating on ML systems research
You’re welcome to join.
Final Thought
Hyperparameter tuning can improve performance.
But defaults decide first impressions.
And in many real systems,
first impressions are all you get.
If you understand how models behave by default,
you make better decisions — faster
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