PredictorLab

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Upload your data. Say what you want to predict. Get an honest model with a confidence interval, a profit-optimized threshold, and a REST API — in 5 minutes.

PredictorLab is self-hosted tabular ML for analysts who need ML but don't have a DS team. You know your data, you know your business costs — PredictorLab handles the ML: catches leakage, splits correctly, calibrates probabilities, and converts them into decisions in rubles.

Spoiler: the model that shows 99% accuracy on your first upload is almost certainly broken. This tool will tell you exactly why — and fix it for you.

Who this is for

What you need: a dataset with a labeled outcome column, and rough numbers for what a correct prediction earns vs what a wrong one costs. That's it.

What you don't need: Python, scikit-learn, or a DS degree. But if you have them — the REST API and MCP server give you full programmatic control.

The Problem

I've watched this scenario play out dozens of times:

1. A business team uploads a customer churn dataset to a mass-market "no-code ML" tool
2. They get ROC-AUC 0.98 — everybody celebrates
3. The model goes to production, predicts nothing useful, and dies quietly

The 0.98 was never real. The dataset had a cancel_date column — filled in only after a customer had already churned. The model learned to read the future, not to predict it. Mass-market no-code tools don't block on this — at best they show a yellow warning that nobody clicks. Enterprise tools like DataRobot do detect leakage, but they cost $$$ and require an implementation team.

The second problem is even more insidious: even with a "good" model, business people get probabilities — not decisions. "This customer has a 73% churn probability." Great. What do I do with that? Call them? Offer a discount? The tool never says, because the tool doesn't know what a phone call costs or what a saved customer is worth.

PredictorLab solves both — in the gap between "too simple to trust" and "too expensive to buy":

How it looks

Leakage Guard in action — the killer feature. Upload the leaky demo dataset, and the system doesn't just warn — it refuses to show results and tells you what to do:

The "What does this mean?" block explains AUC, lift, and conformal coverage in business terms — not jargon.

Training panel — real-time SSE progress, 7 stages with descriptions, live leaderboard, elapsed/remaining time:

Profit optimization — the threshold is set where your money is maximized, not where F1 looks pretty:

Sharing predictions — three ways, each one click. Web form for non-technical users, API key for developers, batch for processing your entire customer base:

Single prediction — probability, conformal interval, reasons in plain language, OOD flag:

Model Card — auto-generated with leakage report, assumptions, limitations. Exportable to Markdown and PDF:

How it works

  ┌────────────────── Web Interface ──────────────────┐
  │  React UI ←SSE→ FastAPI + asyncio jobs + ProcessPool │
  │         │                                          │
  │         │   ┌─────── REST API :8000 ────────┐      │
  │         │   │  /api/v1/models/{id}/predict  │      │
  │         │   │  /api/v1/models/{id}/whatif   │      │
  │         │   │  /api/v1/jobs/{id}            │      │
  │         │   └───────────────────────────────┘      │
  │         │                                          │
  │         │   ┌─────── MCP Server :8100 ──────┐      │
  │         │   │  predict, explain, what_if,   │      │
  │         │   │  counterfactual, model_card   │      │
  │         │   └───────────────────────────────┘      │
  │         │                                          │
  │         ▼                                          │
  │  ┌─── Core Pipeline ──────────────────────────┐    │
  │  │                                            │    │
  │  │  CSV/XLSX/Parquet ← PII scanner            │    │
  │  │       ↓                                    │    │
  │  │  Feature Builder (point-in-time SQL)       │    │
  │  │       ↓                                    │    │
  │  │  Leakage Guard (9 deterministic checks)    │    │
  │  │       ↓                                    │    │
  │  │  Splits (OOT / group / stratified)         │    │
  │  │       ↓                                    │    │
  │  │  Training (CatBoost + LightGBM + Optuna)    │    │
  │  │       ↓                                    │    │
  │  │  Calibration + Conformal + OOD + SHAP       │    │
  │  │       ↓                                    │    │
  │  │  Profit → Threshold → Counterfactuals       │    │
  │  │       ↓                                    │    │
  │  │  Monitoring (PSI drift + live metrics)      │    │
  │  │                                            │    │
  │  └────────────────────────────────────────────┘    │
  │                                                    │
  │  DuckDB (data) · SQLite (metadata) · joblib       │
  │  PostgreSQL / MySQL / ClickHouse / SQLite (RO)     │
  └────────────────────────────────────────────────────┘

1. Upload CSV, XLSX or Parquet — encoding and delimiter detected automatically (including cp1251). PII columns (email, phone, INN, SNILS, passport, card) are detected and excluded from training by default.
2. Pick a target — from a dropdown or describe it in words (LLM suggests the column and SQL).
3. Leakage Guard runs 9 deterministic checks. If BLOCK — you see exactly which columns leak and why. One click: "Exclude and retrain". Advanced users can override (recorded in Model Card).
4. Train — Quick (CatBoost + linear, ~2 min) or Full (Optuna searches CatBoost + LightGBM within a time budget). Watch live progress: stages, candidate leaderboard, elapsed time.
5. Result — AUC/MAE vs baseline, conformal coverage, calibration curve, SHAP drivers. Set the cost matrix → profit curve → optimal threshold. Try What-If sliders.
6. Share — create a web form link, an API key, or batch-predict an entire file.
7. Monitor — PSI drift alerts, live metrics on real outcomes, webhooks, one-click retrain.

Leakage Guard: the demo that sells the product

The demo-data/b2b_leaky.csv dataset contains a deliberate trap: cancel_date is filled only for customers who already churned. Here's what happens:

Leakage Guard — Verdict: BLOCK

[x] cancel_date almost perfectly predicts the target (AUC 0.998).
    Value appears to be known only after the event has occurred.
[x] final_status: column name resembles a post-event feature.
[x] cancel_date contains dates after the event time (100% of