> ML_ALGORITHM // LOGISTIC-REGRESSION_v1.0
Logistic Regression (Logit)
The industry-standard baseline for regulated binary probability modeling using a logit link function.
Linear & Generalized Modelsclassical-supervisedhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(epochs * n * p) via L-BFGS or SGD
Inference Complexity:O(p)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Exponentiated coefficients directly reflect Odds Ratios per unit predictor increase.
Suitable Tasks & Supported Modalities
Suitable Tasks:
binary classificationmulticlass classification
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
The Regression Analysis of Binary SequencesDavid R. Cox (1958) · Journal of the Royal Statistical Society: Series B (Methodological)
Common Pitfalls & Warnings
- Perfect separation causing coefficient explosion without L2 regularization
- Treating uncalibrated probability thresholds as true calibrated probabilities