README.md

metadata

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.