Logistic Regression: How Machine Learning Predicts Binary Outcomes Like Engine Failure

Logistic Regression is a machine learning technique used to answer binary classification questions — such as whether an engine will fail — unlike Linear Regression, which predicts continuous numeric values. A key limitation of applying linear regression to binary outcomes is that predictions can fall outside the valid 0–1 probability range, which Logistic Regression resolves using the Sigmoid function to constrain outputs between 0% and 100%. The model converts the resulting probability into a final classification using a decision threshold, defaulting to 0.5, though this can be lowered in high-stakes scenarios to trigger earlier warnings. In engine diagnostics, multiple sensor inputs — such as temperature, oil pressure, and vibration — are each assigned a weight, combined linearly, and passed through the Sigmoid curve to produce a unified failure probability. Python's scikit-learn library can be used to train such a model on telemetry data and generate real-time failure risk estimates from live sensor readings.