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Assessing the relationship between a predictor variable and a target variable is an essential task in the model building process. If the relationship is identified and tractable, then the predictor variable is re-expressed to reflect the uncovered relationship, and consequently tested for inclusion into the model. Most methods of variable assessment are based on the well-known correlation coefficient, which is often misused because its linearity assumption is not tested. The purpose of this article is to illustrate a genetic data mining method – the GenIQ Model© – that is perhaps the best “data-straightener” available today. I use the third pair of x and y values from the well-known Anscombe data.
OUTLINE
I. Ancombe Data
ID x y
1 10 7.46
2 8 6.77
3 13 12.74
4 9 7.11
5 11 7.81
6 14 8.84
7 6 6.08
8 4 5.39
9 12 8.15
10 7 6.42
11 5 5.73
II. GenIQ Model (Tree Display)
The GenIQ Model (Code)
x1 = .6550772;
x2 = x;
If x1 NE 0 Then x1 = x2 / x1; Else x1 = 1;
x2 = x;
x3 = x;
x2 = x2 + x3;
x2 = Cos(x2);
x1 = x1 + x2;
GenIQvar(y) = x1;
III. GenIQ Model Results
The results of the GenIQ Model as a data-straightener are in Table 2. There is a perfect rank-order prediction based on the descending GenIQ model score GenIQvar(y), which is used to order the table.
Table 2. GenIQ Model Rank-order Prediction
ID x y GenIQvar(y)
3 13 12.74 20.4919
6 14 8.84 20.4089
9 12 8.15 18.7426
5 11 7.81 15.7920
1 10 7.46 15.6735
4 9 7.11 14.3992
2 8 6.77 11.2546
10 7 6.42 10.8225
7 6 6.08 10.0031
11 5 5.73 6.7936
8 4 5.39 5.9607
Perhaps, the best way of illustrating the GenIQ Model as a data-straightener, and a data mining tool are the plots below:
Plot y*x and Plot GenIQ*x.
IV. Summary
Perhaps, the GenIQ Model is an excellent data-straightener and data mining tool all-in-one? What do you think?
Oh, two things - the correlation coefficients between y and x, and GenIQ(y) and x are: 0.81629 and 0.9895, respectively.
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br@dmstat1.com