Regression estimator. Linear Model trained with L1 regularization technique (Lasso).
Attention: Available only with Activate commercial edition.
Syntax
parameters = lassofit(X,y)
parameters = lassofit(X,y,options)
Inputs
- X
- Training data.
- Type: double
- Dimension: vector | matrix
- y
- Target values.
- Type: double
- Dimension: vector | matrix
- options
- Type: struct
-
- alpha
- Constant that multiplies the L1 term (default: 1). Defaults to 1.0. If alpha=0, it’s equivalent to Ordinary Least Square. Instead of setting alpha=0, linearFit can be used.
- Type: double
- Dimension: scalar
- normalize
- If true, input data X will be normalized before performing regression (default: false). It’s done by subtracting mean and dividing by the standard deviation.
- Type: Boolean
- Dimension: logical
- max_iter
- Maximum number of iterations.
- Type: integer
- Dimension: scalar
- tol
- Tolerance for the optimization. If the updates are smaller than tol the optimization code checks the dual gap for optimality and continues until it is smaller than the tolerance.
- Type: double
- Dimension: scalar
- positive
- If set to true, forces the coefficients to be positive (default: false).
- Type: Boolean
- Dimension: logical
- random_state
- Selects a random feature to update. random_state is the seed used by the random number generator.
- Type: integer
- Dimension: scalar
- selection
- If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially. This ('random') often leads to significantly faster convergence especially when tol is higher than 1e-4. Default: 'cyclic'
- Type: char
- Dimension: string
Outputs
- parameters
- Contains all the values passed to lassofit method as options. Additionally it has below key-value pairs.
- Type: struct
-
- scorer
- Function handle pointing to r2 function (R2 Coefficient of Determination).
- Type: function handle
- intercept
- Constants in decision function.
- Type: integer
- Dimension: scalar
- coef
- Parameter vector.
- Type: double
- Dimension: n_features
- params
- Parameter vector with intercept.
- Type: double
- Dimension: n_features + 1
- n_iter
- Number of iterations run by the coordinate descent solver to reach the specified tolerance.
- Type: integer
- Dimension: scalar
- 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 lassofit 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.normalize = true;
parameters = lassofit(X, y, options)
parameters = struct [
alpha: 1
coef: [Matrix] 1 x 3
0 0 0
intercept: 4
n_features: 3
n_iter: 1
n_samples: 7
normalize: 1
params: [Matrix] 1 x 4
4 0 0 0
positive: 0
scorer: @r2
selection: cyclic
]
Comments
Coordinate descent is used to fit the model. Coordinate descent is an algorithm that considers each column of data at a time. Output 'parameters' can be passed to lassopredict function.