1.0 - 8.00 - LAR Example 2: FitMethod ('lasso') - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
English (United States)

Input

SQL Call

SELECT * FROM LAR (
  ON diabetes AS InputTable
  OUT TABLE OutputTable (diabetes_lasso)
  USING
  TargetColumns ('y', 'age', '[2:5]', 'ldl', 'hdl', '[8:10]')
  FitMethod ('lasso')
  Intercept ('true')
  L2Normalization ('true')
  MaxIterNum (20)
) AS dt;

Output

message
Successful.
Result has been stored in the table specified in the argument OutputTable.

This query returns the following table:

diabetes_lasso
steps var_id var_name max_abs_corr step_length intercept age sex bmi map tc ldl hdl tch ltg glu
1 3 bmi 949.435 60.1193 152.133 0 0 60.1193 0 0 0 0 0 0 0
2 9 ltg 889.316 513.224 152.133 0 0 361.895 0 0 0 0 0 301.775 0
3 4 map 452.901 175.553 152.133 0 0 434.758 79.2364 0 0 0 0 374.916 0
4 7 hdl 316.074 259.367 152.133 0 0 505.66 191.27 0 0 -114.101 0 439.665 0
5 2 sex 130.131 88.6592 152.133 0 -74.9165 511.348 234.155 0 0 -169.711 0 450.667 0
6 10 glu 88.7824 43.6779 152.133 0 -111.979 512.044 252.527 0 0 -196.045 0 452.393 12.0781
7 5 tc 68.9652 135.984 152.133 0 -197.757 522.265 297.16 -103.946 0 -223.926 0 514.75 54.7677
8 8 tch 19.9813 54.0156 152.133 0 -226.134 526.885 314.389 -195.106 0 -152.477 106.343 529.916 64.4874
9 6 ldl 5.47747 5.56726 152.133 0 -227.176 526.391 314.95 -237.341 33.6284 -134.599 111.384 545.483 64.6067
10 1 age 5.08918 41.9996 152.133 -5.71894 -234.398 522.649 320.343 -554.266 286.736 0 148.9 663.033 66.331
11 -7 hdl 2.18225 7.2707 152.133 -7.01124 -237.101 521.075 321.549 -580.439 313.862 0 139.858 674.937 67.1794
12 7 hdl 1.31044 27.97 152.133 -10.0122 -239.819 519.84 324.39 -792.184 476.746 101.045 177.064 751.279 67.6254

The following figure represents the results and shows how the standardized coefficients evolved during the model-building process. The x-axis represents the ratio of the norm of the current beta to the full beta. The y-axis represents the standardized coefficients, which are estimated when standardized predictors are used. The numbers on the top of the graph represent the steps of the model-building process. The numbers on the right represent the predictor IDs.