Run every model.
Trust the one you pick.
Upload your data and benchmark every applicable model at once — and, unlike black-box AutoML, every result is checked for leakage and overfitting before it’s ranked. Plus hyperparameter tuning, prediction, what-if simulation, and a one-click research report.
Still running statistical models one at a time?
Model Lab runs every applicable model at once, comparing them on the same scale to find the best fit.
01 · MODEL COMPARISON
Run every applicable model. Pick the best.
One dataset. Every applicable model. One ranking.
Upload your data and Model Lab runs all applicable models simultaneously — classification or regression. A composite score ranks every model, identifying the best-performing model without manual trial and error.
Model Ranking
iris.csv · Classification
| # | MODEL | ACCURACY | F1 | CV |
|---|---|---|---|---|
| 🥇 | LDA | 100.0% | 1.000 | 98.0±2.7% |
| 🥈 | Random Forest | 97.3% | 0.973 | 96.1±3.1% |
| 🥉 | SVM | 96.7% | 0.967 | 95.3±4.5% |
| 4 | GBM | 95.7% | 0.957 | 95.0±3.8% |
| 5 | Decision Tree | 93.3% | 0.933 | 95.3±3.4% |
| 6 | KNN | 90.0% | 0.900 | 94.7±2.7% |
02 · GUARDRAILS
A 100% score is often a red flag, not a victory.
We make sure it's real — not just impressive.
Most tools celebrate a perfect score. Model Lab does the opposite: it flags target leakage, duplicated targets, and suspicious perfect scores, then excludes those models from the leaderboard and recommendations. A trust layer that chatbots simply don't provide.
Leakage suspected — excluded from Best Model
03 · DIAGNOSTICS & READINESS
Will your model perform well on real-world data?
Every model is checked, then given a clear go/no-go verdict.
Model Lab evaluates overfitting risk, CV stability, sample adequacy, and predictive performance — then combines them into a deployment-readiness verdict. Green, amber, or red. No ambiguity.
Model Health
LDA · Deployment readiness
Accuracy
100.0%
Generalization Gap
2.0% Good
Auto Recommendation
iris.csv
Recommended Model
Linear Discriminant Analysis (LDA)
With n=150 and 4 numeric features, LDA handles linear boundaries exceptionally well.
WHY THIS MODEL
WATCH OUT FOR
04 · ALGORITHM ADVISOR
Find the right model for your data. Know exactly what to do next.
Context-aware suggestions, not generic advice.
Model Lab reads your dataset — sample size, variable count, class balance — and recommends the right algorithm. After you find the winner, it points you to the next analysis (feature importance, decision tree, segmentation) and takes you there in one click.
Feature Importance
SHAP · LDA
Mean |SHAP| values · n=150
05 · FEATURE IMPORTANCE & COMPARISON
Which variable actually moves the needle?
SHAP values, permutation importance — and cross-model agreement.
See model-native importance and SHAP values for every predictor. Then compare importance across all models in one table — variables that consistently rank highly across models are the signals you can trust.
06 · HYPERPARAMETER TUNING
Get more out of your best model.
Smart search within a time budget — then re-checked for leakage.
Pick a preset — Fast, Balanced, or Thorough — and Model Lab tunes the winning model with a randomized hyperparameter search within your chosen time budget. It shows the before/after gain, the best parameters, and runs the guardrails again so a higher score never hides leakage.
Thorough preset · 120 trials · re-checked clean
07 · PREDICT & SIMULATE
Your model doesn’t vanish when the analysis ends.
Save your model, predict new data, and explore what-if scenarios.
Every trained model is saved and reusable. Predict on new data one row at a time or in bulk, compare how different models predict the same rows, and see how predictions change as you change inputs — clearly labeled as model response, not causation.
What-if Simulator
Predicted Species · confidence 52%
Sweep response · Sepal.Width
Model response, not a causal effect.
Auto Report
iris.csv
Winner Summary
🏆 Linear Discriminant Analysis (LDA)
Composite Score: 99.4 · Accuracy: 100.0% · CV: 98.0%±2.7%
AI Interpretation
The LDA model achieved excellent performance (Accuracy=100.0%, CV=98.0%±2.7%) with strong generalization (Gap=2.0%). Petal.Length was the most influential feature (0.5 SHAP)...
08 · AUTO REPORT
One click. A research-grade report.
Download a complete model comparison report.
Model Lab generates a structured report covering winner summary, full rank table, diagnostic results, guardrail checks, feature importance, and an AI-generated interpretation.
EVERYTHING IN ONE RUN
Everything Model Lab does.
Upload once — benchmark, validate, tune, predict, and report.
Compare every model at once
XGBoost, RF, GBM, Decision Tree, SVM, KNN, Naive Bayes, LDA, AdaBoost, LightGBM, CatBoost, MLP, Voting/Stacking, Elastic Net, and more — auto-run.
Composite-score leaderboard
Objective ranking based on Accuracy, F1, AUC, R², CV, and generalization gap.
Best-model recommendation
"So which model should I use?" — with the why and what to watch out for.
Leakage guardrails
Flags target leakage, duplicated targets, suspicious perfect scores — and automatically removes them from the rankings.
Diagnostics & deployment verdict
Overfitting, CV stability, sample adequacy → Ready / Caution / Not ready.
Feature importance (SHAP)
SHAP and permutation importance, plus a cross-model "robust" agreement table.
Hyperparameter tuning
Optuna presets (Fast / Balanced / Thorough) with a post-tuning leakage re-check.
Predict new data
Single-row form or CSV batch scoring, downloadable.
Compare model predictions
Score the same rows with several models and see where they disagree.
What-if simulator
Change inputs and watch predictions respond (model response, not causation).
Next-analysis suggestions
Jump straight to feature importance, decision trees, or K-means clustering with one click.
One-click AI report
Winner summary, rankings, diagnostics, guardrails, importance, and an AI interpretation.