Скачать или смотреть 2- Prospectivity Mapping with Python: From Balanced Labels to Probability Maps

In this video, we build an end-to-end Python workflow for mineral prospectivity using a clean, leakage-safe pipeline:

🔹 Setup & Feature Safety — define target, drop meta and all TRAINING_* leak sources
🔹 Balanced Labels — 1:1 positives vs sampled negatives
🔹 Preprocessing Pipeline — impute + log1p clip + quantile normalize + scale + one-hot geology
🔹 Quick EDA — missingness, histograms, PCA sanity check
🔹 Test & Score — compare SVM, RandomForest, AdaBoost, MLP on a stratified split
🔹 Train & Save — fit the winner (RandomForest) and persist with joblib
🔹 Predict Full Grid — per-cell probabilities across the H3 domain
🔹 Quantile Binning — 80/10/10 or 80/15/5 low/medium/high coverage
🔹 Validate “High” — recall, precision proxy, and selectivity (area filtered)
🔹 Export — CSV for GIS; map-ready artifacts

▶️ This workflow is transparent, reproducible, and ideal for:

Junior geologists and exploration professionals
Researchers building geoscience ML workflows
Anyone deploying data-driven prospectivity maps

🗺️ By the end, you’ll know how to go from CSV features to map-ready probability bins using open-source tools.

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Disclaimer
This video is for educational and informational purposes only. The content represents general knowledge and personal opinions and should not be taken as professional advice. While care has been taken to ensure accuracy, no guarantee is given, and the creator accepts no liability for errors or omissions. Viewers should verify information independently before relying on it.