Logistic Regression

Logistic Regression models the probability of a data point belonging to a particular class using a sigmoid function. Despite its name, it is used for classification, not regression.

It is fast, interpretable, and an excellent baseline model for binary classification tasks.

• Logistic Regression works best when the relationship between features and the target class is approximately linear. If you get convergence warnings, try increasing the iteration count.
• Fast and interpretable always a good first model to try before moving to more complex algorithms.

Use Cases

• Binary classification: Determine whether a structural element passes or fails a given criteria (e.g., area threshold).
• Material suitability: Predict whether a material is suitable (yes/no) for a given structural condition.