ML

Standalone, causal-free supervised-learning library: basic regression methods (linear least squares and series/sieve, ridge, lasso, L²-ball ERM rates, logistic, kernel ridge, feedforward networks, finite-partition predictor ensembles) as first-class objects, each with its defining-optimization, closed-form/structure, and population-target properties. It also provides proper and strictly proper binary losses with their population minimizers. Built on a dual-view spine (a parametric predictor and an extensional hypothesis class joined by a bridge). Imports nothing from the causal layers.

Core 28 core · 15 supporting · 8 submodules Roll-up of the causal-free machine-learning spine: losses, parametrized predictors and extensional hypothesis classes, empirical/population risk, the ERM-minimizer predicates, the parametric↔extensional bridge, the conve Linear 11 core · 4 supporting · 5 submodules Roll-up of the linear-in-features regression family: ordinary least squares and ridge, including finite-sample optimization, closed-form normal-equation solutions, and population-risk target results. Binary 9 core · 7 supporting · 3 submodules Roll-up of the logistic part of Causalean.ML: convexity and compact-set existence for empirical logistic risk, pointwise Fisher consistency of the Bernoulli cross-entropy loss, and the root-n L² estimation rate for an L² Causal­Application 8 core · 2 supporting · 4 submodules This roll-up is the causal-application layer for Causalean.ML, kept separate from the causal-free core. Kernel 6 core · 7 supporting · 3 submodules Roll-up: the abstract RKHS interface, the kernel-ridge representer theorem, effective dimension, and the RKHS-ball Rademacher-complexity bound. Lasso 23 core · 5 supporting · 7 submodules Roll-up of the lasso family: l1penalty, lassoObjective, convexity of the finite objective, the scalar soft-thresholding optimality theorem softThreshold_isMinOn, the fixed-design restricted-eigenvalue oracle inequality, Margin 1 core · 0 supporting · 1 submodule Roll-up: the Rademacher excess-risk rate for empirical minimization of a Lipschitz margin surrogate φ(y·⟪w,x⟩) over the W-ball of linear classifiers. Neural­Net 8 core · 6 supporting · 2 submodules Roll-up for dense affine layers, activations carrying Lipschitz constants, and uniform-width feedforward network evaluation. Partition­Predictor 6 core · 0 supporting · 2 submodules Roll-up: finite-partition (piecewise-constant) predictors and their fixed-average ensembles, with the structural range-preservation property. Ridge 14 core · 9 supporting · 4 submodules Ridge regression in a finite feature basis: penalized least squares with penalty λ‖β‖², in the sample and in the population. Surrogate 6 core · 4 supporting · 2 submodules Two tools for replacing a loss by a better-behaved one.