View Source Scholar.Linear.RidgeRegression (Scholar v0.3.0)

Linear least squares with $L_2$ regularization.

Minimizes the objective function: $$ ||y - Xw||^2\_2 + \alpha||w||^2\_2 $$

Where:

$X$ is an input data
$y$ is an input target
$w$ is the model weights matrix
$\alpha$ is the parameter that controls level of regularization

Time complexity is $O(N^2)$ for :cholesky solver and $O((N^2) * (K + N))$ for :svd solver, where $N$ is the number of observations and $K$ is the number of features.

Summary

Functions

fit(x, y, opts \\ [])

Fits a Ridge regression model for sample inputs x and sample targets y.

predict(model, x)

Makes predictions with the given model on input x.

Functions

fit(x, y, opts \\ [])

Fits a Ridge regression model for sample inputs x and sample targets y.

Options

:sample_weights - The weights for each observation. If not provided, all observations are assigned equal weight.
:fit_intercept? (boolean/0) - If set to true, a model will fit the intercept. Otherwise, the intercept is set to 0.0. The intercept is an independent term in a linear model. Specifically, it is the expected mean value of targets for a zero-vector on input. The default value is true.
:solver - Solver to use in the computational routines:
- :svd - Uses a Singular Value Decomposition of A to compute the Ridge coefficients. In particular, it is more stable for singular matrices than :cholesky at the cost of being slower.
- :cholesky - Uses the standard Nx.LinAlg.solve function to obtain a closed-form solution.
The default value is :svd.
:alpha - Constant that multiplies the $L_2$ term, controlling regularization strength. :alpha must be a non-negative float i.e. in [0, inf).
If :alpha is set to 0.0 the objective is the ordinary least squares regression. In this case, for numerical reasons, you should use Scholar.Linear.LinearRegression instead.
The default value is 1.0.

Return Values

The function returns a struct with the following parameters:

:coefficients - Estimated coefficients for the linear regression problem.
:intercept - Independent term in the linear model.

Examples

iex> x = Nx.tensor([[1.0, 2.0], [3.0, 2.0], [4.0, 7.0]])
iex> y = Nx.tensor([4.0, 3.0, -1.0])
iex> Scholar.Linear.RidgeRegression.fit(x, y)
%Scholar.Linear.RidgeRegression{
  coefficients: Nx.tensor(
    [-0.4237867593765259, -0.6891377568244934]
  ),
  intercept: Nx.tensor(
    5.6569366455078125
  )
}

predict(model, x)

Makes predictions with the given model on input x.

Examples

iex> x = Nx.tensor([[1.0, 2.0], [3.0, 2.0], [4.0, 7.0]])
iex> y = Nx.tensor([4.0, 3.0, -1.0])
iex> model = Scholar.Linear.RidgeRegression.fit(x, y)
iex> Scholar.Linear.RidgeRegression.predict(model, Nx.tensor([[2.0, 1.0]]))
Nx.tensor(
  [4.120225429534912]
)