metadata
language:
- en
tags:
- tabular-regression
- reinforcement-learning
- contextual-bandit
- xgboost
- scikit-learn
- explainable-ai
- shap
- portfolio-optimization
license: mit
pipeline_tag: tabular-regression
🧠 Portfolio Intelligence Engine
High-Performance Pure ML & DRL Models for Portfolio Builder 2.0
Official model registry for Portfolio Builder 2.0. This repository hosts pre-trained statistical scoring, explainability, and dynamic layout policy models.
📊 Models & Artifacts in this Repository
| Artifact | Technology | Purpose | Size |
|---|---|---|---|
ats_xgboost.joblib |
XGBoost Regressor + SHAP | Predicts portfolio competitiveness score (0-100) with local additive feature attribution | ~300 KB |
skill_knn.joblib |
k-NN Graph (Cosine) | Computes tech-stack co-occurrence affinity and detects missing skill gaps | ~80 KB |
bandit_priors.json |
LinUCB Matrix Priors | Pre-calibrated covariance matrices and bias vectors across 6 candidate layout actions | ~15 KB |
feature_schema.json |
JSON Schema | 24-feature definitions, types, and scoring rubrics | ~5 KB |
🎯 Model Performance & Evaluation
- Benchmark Evaluation: R² Score: 0.9271 | RMSE: 3.2000 points
- Inference Latency: < 15 ms (FastAPI on Render Free-Tier CPU)
- Memory Footprint: ~135 MB RAM
- DRL Exploration: 20,000 simulated recruiter interaction episodes across 3 personas (HR Skimmer, Tech Lead, Product Reviewer).
💻 Quickstart with huggingface_hub
import joblib
from huggingface_hub import hf_hub_download
# Download XGBoost model package
model_path = hf_hub_download(
repo_id="sk22082001/portfolio-intelligence-engine",
filename="ats_xgboost.joblib"
)
package = joblib.load(model_path)
model = package["model"]
feature_names = package["feature_names"]
print(f"Loaded {len(feature_names)} feature model successfully!")
🛡️ License & Maintenance
Maintained by the Portfolio Builder 2.0 Engineering Team. Licensed under the MIT License.