Safe Haskell	None
Language	Haskell2010

DataFrame.LinearModel

Description

Linear models for the dataframe ecosystem: regression (OLS, ridge, lasso, elastic net) and one-vs-rest logistic classification. Re-exports the focused submodules.

Synopsis

module DataFrame.LinearModel.Regression
module DataFrame.LinearModel.Logistic
data SolverConfig = SolverConfig {
- scL1Lambda :: !Double
- scL2Lambda :: !Double
- scMaxIter :: !Int
- scTol :: !Double
- scSampleWeights :: !(Maybe (Vector Double))
}
defaultSolverConfig :: SolverConfig
data LinearModel = LinearModel {
- lmWeights :: !(Vector Double)
- lmIntercept :: !Double
- lmFeatureNames :: !(Vector Text)
}

Documentation

module DataFrame.LinearModel.Regression

module DataFrame.LinearModel.Logistic

data SolverConfig #

Hyper-parameters for the FISTA solver.

Constructors

SolverConfig

Fields

scL1Lambda :: !Double
Strength of the L1 penalty on weights (intercept is not regularized).
scL2Lambda :: !Double
Strength of the L2 penalty (λ₂/2)·|w|² (Elastic Net; Zou & Hastie 2005). Combined with scL1Lambda this is the elastic-net objective; 0 reduces the solver to pure L1.
scMaxIter :: !Int
Maximum number of FISTA iterations.
scTol :: !Double
Convergence tolerance on the weight delta (L-inf norm).
scSampleWeights :: !(Maybe (Vector Double))
Optional per-row sample weights, length n (Nothing is uniform). Weights should have mean 1 (i.e. Σ w_i = N) so the Lipschitz bound stays valid; see fitLinearCandidate for the class-balanced construction.

Instances

Instances details

Show SolverConfig
Instance details Defined in DataFrame.LinearSolver Methods showsPrec :: Int -> SolverConfig -> ShowS # show :: SolverConfig -> String # showList :: [SolverConfig] -> ShowS #
Eq SolverConfig
Instance details Defined in DataFrame.LinearSolver Methods (==) :: SolverConfig -> SolverConfig -> Bool # (/=) :: SolverConfig -> SolverConfig -> Bool #

defaultSolverConfig :: SolverConfig #

data LinearModel #

A fitted linear classifier: predicts the positive class when sum (weights .* features) + intercept > 0. Weights of exactly 0 mark features dropped by the L1 penalty (filtered out by modelToExpr).

Constructors

LinearModel
Fields lmWeights :: !(Vector Double) lmIntercept :: !Double lmFeatureNames :: !(Vector Text)

Instances

Instances details

Show LinearModel
Instance details Defined in DataFrame.LinearSolver Methods showsPrec :: Int -> LinearModel -> ShowS # show :: LinearModel -> String # showList :: [LinearModel] -> ShowS #
Eq LinearModel
Instance details Defined in DataFrame.LinearSolver Methods (==) :: LinearModel -> LinearModel -> Bool # (/=) :: LinearModel -> LinearModel -> Bool #