The RidgeRegressionMV companion object provides factory methods for creating Multi-Variate (MV) Regression models.
Attributes
- Companion
- class
- Graph
-
- Supertypes
- Self type
-
RidgeRegressionMV.type
Members list
Value members
Concrete methods
Create a Ridge RegressionMV object from a combined data-response matrix.
Create a Ridge RegressionMV object from a combined data-response matrix.
Value parameters
- col
-
the first designated response column (defaults to next to last column)
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters (defaults to Regression.hp)
- xy
-
the combined data-response matrix (predictors and response)
Attributes
Create a Ridge RegressionMV from a data matrix and response vector. This function centers the data.
Create a Ridge RegressionMV from a data matrix and response vector. This function centers the data.
Value parameters
- fname
-
the feature/variable names (defaults to null)
- hparam
-
the shrinkage hyper-parameter (0 => OLS) in the penalty term 'lambda * b dot b'
- x
-
the un-centered data/input m-by-n matrix, NOT augmented with a first column of ones
- y
-
the un-centered response/output matrix
Attributes
Create a Ridge RegressionMV object from a data matrix and a response matrix. This method provides data rescaling.
Create a Ridge RegressionMV object from a data matrix and a response matrix. This method provides data rescaling.
Value parameters
- fname
-
the feature/variable names (use null for default)
- hparam
-
the hyper-parameters (defaults to Regression.hp)
- x
-
the data/input m-by-n matrix (augment with a first column of ones to include intercept in model)
- y
-
the response/output matrix