Owl_regression_generic_sig.Sig
module Optimise : Owl_optimise_generic_sig.Sig
type arr = Optimise.Algodiff.A.arr
Type of ndarray values.
type elt = Optimise.Algodiff.A.elt
Type of scalar values.
Regression models
ols ?i x y
performs Ordinary Least Squares (OLS) regression on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.x
is the matrix of input features.y
is the vector of output values. Returns an array of coefficients for the linear model.ridge ?i ?alpha x y
performs Ridge regression on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.alpha
is the regularization strength parameter. The default value is 1.0.x
is the matrix of input features.y
is the vector of output values. Returns an array of coefficients for the linear model.lasso ?i ?alpha x y
performs Lasso regression on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.alpha
is the regularization strength parameter. The default value is 1.0.x
is the matrix of input features.y
is the vector of output values. Returns an array of coefficients for the linear model.elastic_net ?i ?alpha ?l1_ratio x y
performs Elastic Net regression on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.alpha
is the regularization strength parameter. The default value is 1.0.l1_ratio
is the ratio between L1 and L2 regularization terms. The default value is 0.5.x
is the matrix of input features.y
is the vector of output values. Returns an array of coefficients for the linear model.svm ?i ?a x y
performs Support Vector Machine (SVM) classification on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.a
is an optional parameter for the regularization parameter (commonly denoted as C). The default value is 1.0.x
is the matrix of input features.y
is the vector of output values. Returns an array of support vectors and coefficients.logistic ?i x y
performs logistic regression on the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.x
is the matrix of input features.y
is the vector of output values. Returns an array of coefficients for the logistic model.exponential ?i x y
fits an exponential model to the data x
and y
.
i
is an optional parameter indicating whether to include an intercept in the model. The default is true
.x
is the vector of input values.y
is the vector of output values. Returns a tuple containing the coefficients of the exponential model.