# elasticnet()

Read（57） Label: elastic net, model, prediction,

Description:

An external library function that builds models and performs predictions using the elastic net regression method.

Syntax:

 elasticnet (X, Y, learning_rate, iterations, l1, l2) The function fits together matrix X and vector Y using the elastic net regression method and returns model information that includes coefficient matrix and and intercept. The model information can act as parameter F in elasticnet (X’, F) to perform a fitting computation. elasticnet (X’, F) The function fits together two matices that have same number of columns – that is, perform predicitions on another matrix X’ using model F, and returns a vector

Parameters:

 X A matrix Y A vector having the same number of rows as matrix X learning_rate Learning rate that is a decimal between 0 and 1; default value is 0.01 iterations Number of iterations; default is 1000 l1 Coefficient 11; default is 0.9 l2 Coefficient 12; default is 0.1 X’ A matrix that has same number of columns as matrix X F The return result of elasticnet(X, Y, learning_rate, iterations, l1, l2)

Return value:

A matrix or a vector

Example:

A

1

[[1.1,1.1],[1.4,1.5],[1.7,1.8],[1.7,1.7],[1.8,1.9],[1.8,1.8],[1.9,1.8],[2.0,2.1],[2.3,2.4],[2.4,2.5]]

2

[16.3,16.8,19.2,18,19.5,20.9,21.1,20.9,20.3,22]

3

=elasticnet(A1,A2,0.01,10000,0.9,0.1) Fit A1 and A2 together using elastic net regression method and return coefficient matrix A3(1) and intercept A3(2)

4

=elasticnet(A1,A3) Perform prediction on A1 using model A3; the result can be compared with actual values in A2