Version Compatibility

Reference examples tested with: pandas 2.2+, scikit-learn 1.4+, xgboost 2.0+, imbalanced-learn 0.12+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

Two high-risk drifts: XGBoost moved early_stopping_rounds from fit() to the constructor (deprecated 1.6, removed from fit() in 2.1); scikit-learn deprecated LogisticRegression(penalty=) in 1.8 (use l1_ratio+C) and CalibratedClassifierCV(cv='prefit') in 1.6 (use FrozenEstimator). If code throws TypeError/FutureWarning, switch to the constructor / l1_ratio / FrozenEstimator form.

Classification Models for Omics Data

"Build a classifier from my expression data" -> Start with a regularized linear model (often the ceiling in p>>n), check for batch shortcuts, and treat the probability -- not the label -- as the product.

• Linear (often best): LogisticRegression(penalty='elasticnet', solver='saga')
• Trees when nonlinear/interaction signal: RandomForestClassifier, xgboost.XGBClassifier
• Imbalance: class_weight='balanced' or threshold tuning, NOT SMOTE for risk models

The Single Most Important Modern Insight -- In p>>n, Simple Often Wins and the Probability, Not the Label, Is the Product

Omics classification almost always lives in p>>n (thousands of features, tens-to-hundreds of samples). Two counterintuitive consequences follow. First, more flexible is not better: with n in the dozens the variance of a flexible learner dominates, the full covariance is singular so QDA/full-LDA are undefined, and simple diagonal/linear methods match or beat elaborate ones (Dudoit 2002). "Random forest is the obvious choice for expression data" is a myth -- SVM/regularized logistic frequently win on microarray-style problems (Statnikov 2008), and gradient-boosted trees beat deep nets on tabular/omics data (Grinsztajn 2022). Regularization is the load-bearing wall, not a tuning nicety.