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---
language:
- en
license: apache-2.0
tags:
- tabular-classification
- gradient-boosting
- predictive-maintenance
- scikit-learn
library_name: sklearn
datasets:
- engine-sensor-data # Replace with your actual dataset name if known
metrics:
- recall
- roc_auc
- pr_auc
---
# Predictive Maintenance – Gradient Boosting Model
## Model Overview
This model is a recall-optimized Gradient Boosting classifier developed to support predictive maintenance for engine systems. The primary objective is to identify engines likely to require maintenance before failure occurs.
## Training Data
The model was trained on a prepared engine sensor dataset sourced from the Hugging Face Dataset Hub. The dataset contains structured numeric sensor readings representing engine operating conditions.
## Objective
- Minimize missed engine failures (false negatives)
- Prioritize recall for the faulty engine class
## Evaluation Metrics
- Recall (Faulty): ~0.84
- ROC-AUC: ~0.70
- PR-AUC: ~0.80
## Intended Use
This model is intended for:
- Predictive maintenance decision support
- Risk-based maintenance scheduling
- Offline or batch inference scenarios
## Limitations
- Trained on a static, pre-processed dataset
- Does not incorporate temporal or sequential dependencies
- Threshold selection may require calibration based on operational risk tolerance
## Model Artifacts
The repository contains a serialized `joblib` model file that can be loaded directly for inference in Python-based environments. |