---
library_name: sklearn
pipeline_tag: tabular-classification
tags:
- predictive-maintenance
- random-forest
- scikit-learn
---

# Predictive Maintenance Random Forest

This model predicts the binary `engine_condition` target from engineered engine sensor features.

## Training

- Algorithm: `RandomForestClassifier`
- Source dataset: `saranka85/predictive-maintenance-engineered-data`
- Selection metric: mean stratified 5-fold CV F1
- Best CV F1: 0.7564
- Best parameters: `{"class_weight": null, "max_depth": 16, "max_features": "sqrt", "min_samples_leaf": 3, "n_estimators": 400}`

## Held-out test metrics

- test_accuracy: 0.6642
- test_precision: 0.6938
- test_recall: 0.8364
- test_f1: 0.7585
- test_roc_auc: 0.6890

## Experiment tracking evidence

The experiment was tracked with MLflow using parent run `9bc671f6b04f4fc783913cc5a0dcd898`. 
The portable reviewer bundle contains:

- `mlflow_runs.csv`: parameters, metrics, tags, and status for the parent and tuning runs
- `random_forest_cv_results.csv`: complete GridSearchCV results
- `classification_report.csv`: per-class held-out test metrics
- `confusion_matrix.png`: held-out test confusion matrix
- `experiment_summary.json`: experiment configuration and best-result summary

Reviewers do not need the local MLflow database; all run parameters, metrics, tags, and statuses are provided in portable CSV and JSON files.

## Usage

Load `model.joblib` with `joblib.load`. Input columns and experiment details are recorded in `model_metadata.json`.