Compare Kernel SVMs and Gradient Boosting on Imbalanced Tabular Data
Overview
What this challenge is about.
Train SVM & gradient boosting on loan-default data, compare via nested CV, and defend a model choice for a verifiable certificate.
The scenario
The startup (Series B, around 90 staff, around EUR 30M annual recurring revenue) sells per-decision pricing and needs predictable inference latency on commodity CPU hardware.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether kernel SVMs still belong in the production credit-risk model zoo or whether gradient boosting wins on every axis that matters.
Earning criteria — what you'll demonstrate
- Apply nested cross-validation for honest model selection
- Compare kernel methods against tree-ensemble methods on real tabular data
- Quantify probability calibration with ECE and reliability diagrams
- Defend a model choice across statistical, calibration, and operational axes
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Machine Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Kernel Methods
Apply kernel methods to solve real industry problems and demonstrate production-level capability.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
- Model Selection
Apply model selection to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Nested Cross Validation
Apply nested cross validation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Rigorous model-selection comparisons with nested CV and calibrated probability outputs are the bread-and-butter of ML research roles at any quant or fintech AI team.
This challenge sharpens
- kernel-methods
- nested-cross-validation
- model-selection
Applied AI Scientist
Trading off accuracy, calibration, and operational latency for a real engineering decision is exactly the applied-AI-scientist's daily reality.
This challenge sharpens
- model-selection
- model-calibration
- gradient-boosting
Data Scientist
Honest reporting of all axes (not just the winning metric) is what hiring managers look for in senior data-scientist candidates.
This challenge sharpens
- model-selection
- model-calibration
- gradient-boosting