Linear regression with L2 regularization.
Attention: Available only with Activate commercial edition.
Syntax
parameters = ridgefit(X,y)
parameters = ridgefit(X,y,options)
Inputs
 X
 Training data.
 Type: double
 Dimension: vector  matrix
 y
 Target values.
 Type: double
 Dimension: vector  matrix
 options
 Type: struct

 to_normalize
 If true, input data X will be normalized before performing regression (default: false). It is done by subtracting mean and dividing by the standard deviation.
 Type: Boolean
 Dimension: logical
 l2_penalty
 Regularization Strength (default: 0). If value greater than 0, it becomes Ridge Regression.
 Type: double  integer
 Dimension: scalar
Outputs
 parameters
 Contains all the values passed to ridgefit method as options. Additionally it has below keyvalue pairs.
 Type: struct

 scorer
 Function handle pointing to r2 function (R2 Coefficient of Determination).
 Type: function handle
 intercept
 Estimated intercept.
 Type: integer
 Dimension: scalar
 coef
 Estimated coefficients
 Type: double
 Dimension: vector
 params
 Contains both intercept and coef as a vector. It is used by predict method.
 Type: double
 Dimension: vector
 n_samples
 Number of rows in the training data.
 Type: integer
 Dimension: scalar
 n_features
 Number of columns in the training data.
 Type: integer
 Dimension: scalar
Example
Usage of ridgefit with options
X = [1 2 3; 4 5 6; 7 8 9; 10 11 12; 13 14 15; 16 17 18; 19 20 21];
y = [1, 2, 3, 4, 5, 6, 7];
options = struct;
options.to_normalize = true;
options.l2_penalty = 0.03;
parameters = ridgefit(X, y, options);
> parameters
parameters = struct [
coef: [Matrix] 3 x 1
0.66572
0.66572
0.66572
intercept: 4
l2_penalty: 0.03
n_features: 3
n_samples: 7
params: [Matrix] 4 x 1
4.00000
0.66572
0.66572
0.66572
scorer: @r2
to_normalize: 1
]
Comments
Output 'parameters' can be passed to ridgepredict function.