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Elastic Net regression

 

Continue to use the sample data in the lasso regression section above, and use elastic net regression to fit

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)

4

=elasticnet(A1,A3)

5

=canvas()

6

=A5.plot("NumericAxis","name":"y_pre","autoCalcValueRange":false,"autoRangeFromZero":false,"maxValue":25,"minValue":15,"title":"prediction data")

7

=A5.plot("NumericAxis","name":"y","location":2,"autoCalcValueRange":false,"autoRangeFromZero":false,"maxValue":25,"minValue":15,"title":"observation data","titleAngle":270)

8

=A5.plot("Dot","lineWeight":0,"lineColor":-16776961,"markerWeight":1,"axis1":"y_pre","data1":A4.conj(),"axis2":"y","data2": A2)

9

=to(15:25)

10

=A5.plot("Line","markerStyle":0,"axis1":"y_pre","data1":A9,"axis2":"y", "data2":A9)

11

=A5.draw(600,600)

A1 Input data x

A2 Input data y

A3 Use elastic net to fit the data, 0.01 represents the learning rate, 10000 represents the number of iterations,0.9 represents the L1 coefficient, 0.1 represents the L2 coefficient, and return the model information

A3(1) The coefficient matrix

A3(2) The intercede

..

A4 Use the model built in A3 to make predictions on A1, return the prediction result.

..

A5-A11 Draw a picture to evaluate the model performance. The horizontal axis represents the predicted value for each sample, and the vertical axis represents the true value of the observed data. If all the points can be evenly distributed near the diagonal, then the fitting value of the equation has little difference from the original value, and the fitting performance of the equation is good.If the model performance is not ideal, it can be optimized by adjusting the learning rate and the number of iterations

..

A5 Generate canvas

A6 Plot the horizontal axis, the prediction value

A7 Draw the vertical axis, the true value of the observation data

A8 Plot point primitives with the horizontal axis is the predicted value and the vertical axis is the true value

A9 Take a fixed interval of [15,25]

A10 Draw a line, y=x

A11 Perform the drawing


 
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