Compare models by decision criteria.
Do not choose a model because it is fashionable. Compare assumptions, data fit, error cost, metrics and production constraints.
Logistic Regression
A probabilistic classification baseline that is transparent, fast and surprisingly competitive.
Random Forest
An ensemble of decorrelated decision trees that captures nonlinear interactions with limited preprocessing.
Learn a weighted score then pass it through a sigmoid to obtain a probability.
Many trees vote so individual tree variance is averaged away.
p(y=1|x)=σ(wᵀx+b)
f(x)=1/T Σₜ fₜ(x)
Binary classification · Interpretable baselines · Calibrated probability starting point
Tabular classification · Tabular regression · Strong nonlinear baseline
Fast · Explainable coefficients · Small memory footprint
Robust · Handles mixed feature scales · Feature importance options
Linear boundary · Interaction engineering may be needed
Large models · Probability calibration can be weak
PR-AUC · ROC-AUC · Log loss · Calibration error
PR-AUC · ROC-AUC · RMSE · Latency
Standardize only when useful · Monitor calibration · Choose thresholds from business cost
Control tree count/depth · Measure inference cost · Validate feature importance carefully