elasticnet()
Description:
Build models and performs predictions using the elastic net regression method.
Syntax:
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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 |
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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 |
Note:
External library function (See External Library Guide).
It builds models and performs predictions using the elastic net regression method.
Parameter:
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X |
A matrix |
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Y |
A vector having the same number of rows as matrix X |
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learning_rate |
Learning rate that is a decimal between 0 and 1; default value is 0.01 |
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iterations |
Number of iterations; default is 1000 |
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l1 |
Coefficient 11; default is 0.9 |
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l2 |
Coefficient 12; default is 0.1 |
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X’ |
A matrix that has same number of columns as matrix X |
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F |
The return result of elasticnet(X, Y, learning_rate, iterations, l1, l2) |
Return value:
Matrix/Vector
Example:
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A |
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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]] |
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2 |
[16.3,16.8,19.2,18,19.5,20.9,21.1,20.9,20.3,22] |
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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). |
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4 |
=elasticnet(A1,A3) |
Perform prediction on A1 using model A3; the result can be compared with actual values in A2. |
