kernel function to apply to create kernel matrix
L2 regularization value
number of columns in each block of BCD
number of epochs of BCD to run
seed used for permuting column blocks in BCD
frequency at which intermediate data should be checkpointed
The type-safe method that ML developers need to implement when writing new Estimators.
The type-safe method that ML developers need to implement when writing new Estimators.
The estimator's training data.
The estimator's training labels
A new transformer
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
The training data
The training labels
A pipeline that fits this label estimator and applies the result to inputs.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
The training data
The training labels
A pipeline that fits this label estimator and applies the result to inputs.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
The training data
The training labels
A pipeline that fits this label estimator and applies the result to inputs.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
The training data
The training labels
A pipeline that fits this label estimator and applies the result to inputs.
Solves a kernel ridge regression problem of the form (K(x, x) + \lambda * I) * W = Y using Gauss-Seidel based Block Coordinate Descent.
The function K is specified by the kernel generator and this class uses the dual formulation of the ridge regression objective to improve numerical stability.