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Last update: February 19, 2014
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Missing Values Mapping
GeneXproTools supports missing values both for numerical and categorical variables.
The supported representations for missing values consist of NULL, Null, null, NA, na, ?,
blank cells, ., ._, and .*, where * can be any letter in lower or upper case.
When data is loaded into GeneXproTools, the missing values are automatically replaced by
zero so that you can start modeling right away. But then GeneXproTools allows you to choose
different mappings through the Missing Values Mapping Window.
In the Missing Values Mapping Window you have access to pre-computed data statistics,
such as the majority class for categorical variables and the average for numerical variables,
to help you choose the most effective mapping.
As mentioned for categorical values, GeneXproTools is not just a useful platform
for trying out different mappings for missing values to see how they impact on
model evolution and then choose the best one: GeneXproTools generates code with support
for missing values that you can immediately deploy without further hassle, allowing you
to use the exact same format that was used to load the data into GeneXproTools. The
sample MATLAB code below shows a classification model of 7 variables, 6 of which with missing values:
%------------------------------------------------------------------
% Classification model generated by GeneXproTools 5.0 on 5/17/2013 6:44:02 PM
% GEP File: D:\GeneXproTools\Version5.0\OnlineGuide\Diabetes_M01.gep
% Training Records: 570
% Validation Records: 198
% Fitness Function: ROC Measure, ROC Threshold
% Training Fitness: 801.044268510405
% Training Accuracy: 75.09% (428)
% Validation Fitness: 842.459561470235
% Validation Accuracy: 77.27% (153)
%------------------------------------------------------------------
function result = gepModel(d_string)
ROUNDING_THRESHOLD = 1444302.57350085;
G1C9 = 8.49354625080111;
G2C0 = -3.496505630665;
G2C6 = 0.893559068575091;
G3C6 = 4.40351573229164;
d = TransformCategoricalInputs(d_string);
varTemp = 0.0;
varTemp = ((gep3Rt(((d(4)-d(1))^3))-(d(8)*(d(6)-G1C9)))^2);
varTemp = varTemp + (((((G2C0+d(2))/2.0)*(d(2)+d(2)))+((G2C6+d(5))*G2C0))*d(2));
varTemp = varTemp + (gep3Rt((d(2)*(((G3C6-d(2))*d(3))-d(5))))^3);
if (varTemp >= ROUNDING_THRESHOLD),
result = 1;
else
result = 0;
end
function result = gep3Rt(x)
if (x < 0.0),
result = -((-x)^(1.0/3.0));
else
result = x^(1.0/3.0);
end
function output = TransformCategoricalInputs(input)
switch char(input(1))
case '.D'
output(1) = 12.0;
case '.E'
output(1) = 11.0;
case '.L'
output(1) = 15.0;
case '.T'
output(1) = 10.0;
case '.Z'
output(1) = 0.0;
otherwise
output(1) = str2double(input(1));
end
switch char(input(2))
case '?'
output(2) = 0.0;
otherwise
output(2) = str2double(input(2));
end
switch char(input(3))
case '?'
output(3) = 0.0;
otherwise
output(3) = str2double(input(3));
end
switch char(input(4))
case '?'
output(4) = 0.0;
otherwise
output(4) = str2double(input(4));
end
switch char(input(5))
case '?'
output(5) = 0.0;
otherwise
output(5) = str2double(input(5));
end
switch char(input(6))
case '?'
output(6) = 0.0;
otherwise
output(6) = str2double(input(6));
end
output(8) = str2double(input(8));
See Also:
Related Tutorials:
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