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 runsrandom_forest_cv_results.csv: complete GridSearchCV resultsclassification_report.csv: per-class held-out test metricsconfusion_matrix.png: held-out test confusion matrixexperiment_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.
- Downloads last month
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from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("saranka85/predictive-maintenance-random-forest", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html