{ From user Lonnie, Model Logistic_Regression_ at 18-Sep-2018 7:18:42 PM, encoding="UTF-8" }
SoftwareVersion 5.1.6


{ System Variables with non-default values: }
SampleSize := 100
TypeChecking := 1
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SaveOptions := 2
SaveValues := 0
{!40300|Sys_DomainSelfIndex := 1}
{!40400|Sys_AllNullTreatment := 1}
{!50000|OutlinerState Sys_FoundSet: 2,19,386,450,536}
{!50016|PlusOnTextReturnsNaN := 0}




Model Logistic_Regression_
Title: Logistic Regression Example
Description: This regression model was originally created during the Analytica User Group Webinar on June 5, 2008.  In 2018, I substantially reorganized it, but it is still based on the original <a href="http://wiki.analytica.com/index.php?title=Tutorial_videos#Logistic_Regression">Logistic Regression Webinar</a>.~
~
The 2018 reorg embedded the data and changed to use the built-in LogisticRegression( ) function, so that users of all Analytica editions can explore and evaluate.~
~
The first module, "Training on all the data", covers the basics of calculating a Regression and Logistic Regression, by calculating on the full data set. Then the next two modules repeat, but train on a subset of the data (the "training set") and test generalization performance the remaining "test set".  The second module uses standard linear regression to predict the classification (which ends up with many points <0 and >1, which we just treat an 0.0 and 1.0). The third module uses Logistic Regression on the same data. ~
~
The generalization score is a type of average that can reasonably be interpreted as an accuracy (where 100% is best possible). ~

Author: Lonnie Chrisman, Ph.D.~
Lumina Decision Systems.
Date: Sun, Jun 01, 2008 10:50 AM
SaveAuthor: Lonnie
SaveDate: Tue, Sep 18, 2018 7:18 PM
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Index Column
Title: Column
Description: The features used for the data fit -- these are the labels for the "basis terms". ~
One thing notable here is that we did not include a constant offset term. Adding one doesn't seem to help, but you can explore this as an interesting exercise.
Definition: ['radius mean','radius SE','radius worst','texture mean','texture SE','texture worst','perimeter mean','perimeter SE','perimeter worst','area mean','area SE','area worst','smoothness mean','smoothness SE','smoothness worst','compactness mean','compactness SE','compactness worst','concavity mean','concavity SE','concavity worst','concave pts mean','concave pts SE','concave pts worst','symmetry mean','symmetry SE','symmetry worst','fractal dim mean','fractal dim SE','fractal dim worst']
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Index Id
Title: ID
Description: Indexes the full data set. Each sample in the full dataset has a unique ID.
Definition: 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Variable Is_malignant
Title: Is Malignant
Description: The classifications, i.e., whether each sample is malignant (=1) or benign (=0), for the full data set. The features are in Breast_cancer_data.
Definition: Table(Id)(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,0,0,0,0,1,1,0,1,1,0,0,0,0,1,0,1,1,0,0,0,0,1,0,1,1,0,1,0,1,1,0,0,0,1,1,0,1,1,1,0,0,0,1,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,1,0,0,0,1,1,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,1,1,0,0,1,1,0,0,0,0,1,0,0,1,1,1,0,1,0,1,0,0,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,0,1,0,1,0,0,1,0,1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,1,0,0,1,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0)
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Variable Breast_cancer_data
Title: Breast cancer data
Description: The breast cancer data set from the UCI ML repository. This table contains the features, whereas Is_malignat contains the classifications. The task is to classify each sample as malignant or not (benign) based on the features in this table.
Definition: Table(Column,Id)(~
17.99,20.57,19.69,11.42,20.29,12.45,18.25,13.71,13,12.46,16.02,15.78,19.17,15.85,13.73,14.54,14.68,16.13,19.81,13.54,13.08,9.504,15.34,21.16,16.65,17.14,14.58,18.61,15.3,17.57,18.63,11.84,17.02,19.27,16.13,16.74,14.25,13.03,14.99,13.48,13.44,10.95,19.07,13.28,13.17,18.65,8.196,13.17,12.05,13.49,11.76,13.64,11.94,18.22,15.1,11.52,19.21,14.71,13.05,8.618,10.17,8.598,14.25,9.173,12.68,14.78,9.465,11.31,9.029,12.78,18.94,8.888,17.2,13.8,12.31,16.07,13.53,18.05,20.18,12.86,11.45,13.34,25.22,19.1,12,18.46,14.48,19.02,12.36,14.64,14.62,15.37,13.27,13.45,15.06,20.26,12.18,9.787,11.6,14.42,13.61,6.981,12.18,9.876,10.49,13.11,11.64,12.36,22.27,11.34,9.777,12.63,14.26,10.51,8.726,11.93,8.95,14.87,15.78,17.95,11.41,18.66,24.25,14.5,13.37,13.85,13.61,19,15.1,19.79,12.19,15.46,16.16,15.71,18.45,12.77,11.71,11.43,14.95,11.28,9.738,16.11,11.43,12.9,10.75,11.9,11.8,14.95,14.44,13.74,13,8.219,9.731,11.15,13.15,12.25,17.68,16.84,12.06,10.9,11.75,19.19,19.59,12.34,23.27,14.97,10.8,16.78,17.47,14.97,12.32,13.43,15.46,11.08,10.66,8.671,9.904,16.46,13.01,12.81,27.22,21.09,15.7,11.41,15.28,10.08,18.31,11.71,11.81,12.3,14.22,12.77,9.72,12.34,14.86,12.91,13.77,18.08,19.18,14.45,12.23,17.54,23.29,13.81,12.47,15.12,9.876,17.01,13.11,15.27,20.58,11.84,28.11,17.42,14.19,13.86,11.89,10.2,19.8,19.53,13.65,13.56,10.18,15.75,13.27,14.34,10.44,15,12.62,12.83,17.05,11.32,11.22,20.51,9.567,14.03,23.21,20.48,14.22,17.46,13.64,12.42,11.3,13.75,19.4,10.48,13.2,12.89,10.65,11.52,20.94,11.5,19.73,17.3,19.45,13.96,19.55,15.32,15.66,15.53,20.31,17.35,17.29,15.61,17.19,20.73,10.6,13.59,12.87,10.71,14.29,11.29,21.75,9.742,17.93,11.89,11.33,18.81,13.59,13.85,19.16,11.74,19.4,16.24,12.89,12.58,11.94,12.89,11.26,11.37,14.41,14.96,12.95,11.85,12.72,13.77,10.91,11.76,14.26,10.51,19.53,12.46,20.09,10.49,11.46,11.6,13.2,9,13.5,13.05,11.7,14.61,12.76,11.54,8.597,12.49,12.18,18.22,9.042,12.43,10.25,20.16,12.86,20.34,12.2,12.67,14.11,12.03,16.27,16.26,16.03,12.98,11.22,11.25,12.3,17.06,12.99,18.77,10.05,23.51,14.42,9.606,11.06,19.68,11.71,10.26,12.06,14.76,11.47,11.95,11.66,15.75,25.73,15.08,11.14,12.56,13.05,13.87,8.878,9.436,12.54,13.3,12.76,16.5,13.4,20.44,20.2,12.21,21.71,22.01,16.35,15.19,21.37,20.64,13.69,16.17,10.57,13.46,13.66,11.08,11.27,11.04,12.05,12.39,13.28,14.6,12.21,13.88,11.27,19.55,10.26,8.734,15.49,21.61,12.1,14.06,13.51,12.8,11.06,11.8,17.91,11.93,12.96,12.94,12.34,10.94,16.14,12.85,17.99,12.27,11.36,11.04,9.397,14.99,15.13,11.89,9.405,15.5,12.7,11.16,11.57,14.69,11.61,13.66,9.742,10.03,10.48,10.8,11.13,12.72,14.9,12.4,20.18,18.82,14.86,13.98,12.87,14.04,13.85,14.02,10.97,17.27,13.78,10.57,18.03,11.99,17.75,14.8,14.53,21.1,11.87,19.59,12,14.53,12.62,13.38,11.63,13.21,13,9.755,17.08,27.42,14.4,11.6,13.17,13.24,13.14,9.668,17.6,11.62,9.667,12.04,14.92,12.27,10.88,12.83,14.2,13.9,11.49,16.25,12.16,13.9,13.47,13.7,15.73,12.45,14.64,19.44,11.68,16.69,12.25,17.85,18.01,12.46,13.16,14.87,12.65,12.47,18.49,20.59,15.04,13.82,12.54,23.09,9.268,9.676,12.22,11.06,16.3,15.46,11.74,14.81,13.4,14.58,15.05,11.34,18.31,19.89,12.88,12.75,9.295,24.63,11.26,13.71,9.847,8.571,13.46,12.34,13.94,12.07,11.75,11.67,13.68,20.47,10.96,20.55,14.27,11.69,7.729,7.691,11.54,14.47,14.74,13.21,13.87,13.62,10.32,10.26,9.683,10.82,10.86,11.13,12.77,9.333,12.88,10.29,10.16,9.423,14.59,11.51,14.05,11.2,15.22,20.92,21.56,20.13,16.6,20.6,7.76,~
10.38,17.77,21.25,20.38,14.34,15.7,19.98,20.83,21.82,24.04,23.24,17.89,24.8,23.95,22.61,27.54,20.13,20.68,22.15,14.36,15.71,12.44,14.26,23.04,21.38,16.4,21.53,20.25,25.27,15.05,25.11,18.7,23.98,26.47,17.88,21.59,21.72,18.42,25.2,20.82,21.58,21.35,24.81,20.28,21.81,17.6,16.84,18.66,14.63,22.3,21.6,16.34,18.24,18.7,22.02,18.75,18.57,21.59,19.31,11.79,14.88,20.98,22.15,13.86,23.84,23.94,21.01,19.04,17.33,16.49,21.31,14.64,24.52,15.79,16.52,19.65,10.94,16.15,23.97,18,20.97,15.86,24.91,26.29,15.65,18.52,21.46,24.59,21.8,15.24,24.02,22.76,14.76,18.3,19.83,23.03,17.84,19.94,12.84,19.77,24.98,13.43,20.52,19.4,19.29,15.56,18.33,18.54,19.67,21.26,16.99,20.76,19.65,20.19,15.83,21.53,15.76,16.67,22.91,20.01,10.82,17.12,20.2,10.89,16.39,17.21,24.69,18.91,16.39,25.12,13.29,19.48,21.54,13.93,21.91,22.47,16.67,15.39,17.57,13.39,11.97,18.05,17.31,15.92,14.97,14.65,16.58,18.77,15.18,17.91,20.78,20.7,15.34,13.08,15.34,17.94,20.74,19.46,12.74,12.96,20.18,15.94,18.15,22.22,22.04,19.76,9.71,18.8,24.68,16.95,12.39,19.63,11.89,14.71,15.15,14.45,18.06,20.11,22.22,13.06,21.87,26.57,20.31,14.92,22.41,15.11,18.58,17.19,17.39,15.9,23.12,21.41,18.22,26.86,23.21,16.33,22.29,21.84,22.49,20.22,19.56,19.32,26.67,23.75,18.6,16.68,17.27,20.26,22.54,12.91,22.14,18.94,18.47,25.56,23.81,16.93,18.35,17.48,21.56,32.47,13.16,13.9,17.53,20.25,17.02,13.47,15.46,15.51,23.97,22.33,19.08,27.08,33.81,27.81,15.91,21.25,26.97,21.46,27.85,39.28,15.6,15.04,18.19,23.77,23.5,19.86,17.43,14.11,25.22,14.93,23.56,18.45,19.82,17.08,19.33,17.05,28.77,17.27,23.2,33.56,27.06,23.06,22.13,19.38,22.07,31.12,18.95,21.84,16.21,20.39,16.82,13.04,20.99,15.67,24.48,17.36,14.16,19.98,17.84,15.18,26.6,14.02,18.18,18.77,15.7,18.4,20.76,13.12,19.96,18.89,19.73,19.1,16.02,17.46,13.78,13.27,12.35,18.14,18.17,23.09,18.9,19.89,23.86,18.61,18.16,24.49,15.82,14.4,12.71,13.84,19.11,15.69,13.37,10.72,18.6,16.85,14.08,18.87,18.9,17,16.18,19.66,13.32,21.51,15.21,17.3,12.88,17.93,20.71,21.88,15.51,19.35,19.86,14.78,19.02,21,14.23,21.43,17.53,24.27,16.54,16.84,14.96,21.68,15.45,14.71,18.9,14.74,16.03,14.96,17.07,19.22,17.46,25.74,14.07,19.07,18.59,16.21,15.49,18.32,18.07,21.57,18.84,18.29,16.95,21.78,26.83,18.02,17.25,21.9,23.29,13.21,15.1,17.35,16.07,16.07,20.22,28.21,15.15,18.83,12.96,14.93,22.72,17.48,13.72,23.29,14.09,16.16,15.5,23.21,12.22,16.84,19.97,22.28,17.72,17.18,18.89,17.46,14.83,17.26,21.02,10.91,18.29,16.17,14.95,18.59,14.86,21.37,20.66,17.92,17.57,16.83,21.68,22.11,29.81,21.17,21.7,21.08,12.17,21.41,19.04,13.98,16.02,19.13,19.12,21.28,14.98,21.98,16.62,17.67,22.53,17.68,19.54,21.97,16.94,19.62,19.54,15.98,19.6,15.66,17.2,25.42,15.79,18.32,16.85,24.89,28.03,17.66,19.34,20.52,21.54,25,28.23,13.98,17.15,30.72,29.29,25.25,25.13,28.2,27.15,26.27,26.99,18.36,18.22,20.13,20.74,18.1,23.33,18.18,18.49,28.14,14.93,29.97,15.62,15.73,20.53,16.62,14.59,19.51,18.03,19.24,14.06,17.64,11.28,16.41,16.85,18.82,16.17,20.2,22.44,13.23,20.56,12.83,20.54,20.21,18.17,17.31,17.52,21.24,16.74,24.49,16.32,19.83,12.87,13.14,20.04,17.12,15.7,23.95,14.69,14.7,20.52,13.66,19.07,18.61,20.58,20.26,18.22,16.7,13.9,21.6,19.83,18.68,15.68,13.1,18.75,12.27,13.17,13.44,17.56,20.02,16.33,20.67,17.62,20.86,22.55,24.44,25.49,25.44,14.44,24.99,25.42,28.06,20.7,23.23,16.35,16.58,19.34,24.21,21.48,22.44,29.43,21.94,28.92,27.61,19.59,27.88,22.68,23.93,27.15,29.37,30.62,25.09,22.39,28.25,28.08,29.33,24.54,~
122.8,132.9,130,77.58,135.1,82.57,119.6,90.2,87.5,83.97,102.7,103.6,132.4,103.7,93.6,96.73,94.74,108.1,130,87.46,85.63,60.34,102.5,137.2,110,116,97.41,122.1,102.4,115,124.8,77.93,112.8,127.9,107,110.1,93.63,82.61,95.54,88.4,86.18,71.9,128.3,87.32,85.42,123.7,51.71,85.98,78.04,86.91,74.72,87.21,75.71,120.3,97.26,73.34,125.5,95.55,82.61,54.34,64.55,54.66,96.42,59.2,82.69,97.4,60.11,71.8,58.79,81.37,123.6,58.79,114.2,90.43,79.19,104.1,87.91,120.2,143.7,83.19,73.81,86.49,171.5,129.1,76.95,121.1,94.25,122,79.78,95.77,94.57,100.2,84.74,86.6,100.3,132.4,77.79,62.11,74.34,94.48,88.05,43.79,77.22,63.95,67.41,87.21,75.17,79.01,152.8,72.48,62.5,82.15,97.83,68.64,55.84,76.53,58.74,98.64,105.7,114.2,73.34,121.4,166.2,94.28,86.1,88.44,87.76,123.4,99.58,130.4,79.08,101.7,106.2,102,120.2,81.72,74.72,73.06,96.85,73,61.24,105.1,73.66,83.74,68.26,78.11,78.99,97.84,93.97,88.12,83.51,53.27,63.78,70.87,85.31,78.27,117.4,108.4,76.84,68.69,76.1,126.3,130.7,79.85,152.1,95.5,68.77,109.3,116.1,96.22,78.85,85.84,102.5,70.21,67.49,54.42,64.6,109.3,82.01,81.29,182.1,142.7,101.2,73.53,98.92,63.76,118.6,74.68,75.27,78.83,94.37,82.02,60.73,81.15,100.4,82.53,90.63,117.4,127.5,94.49,78.54,115.1,158.9,91.56,81.09,98.78,62.92,109.7,87.02,98.17,134.7,75.51,188.5,114.5,92.87,90.96,77.32,65.05,129.7,128,87.88,88.59,65.12,102.6,84.55,92.51,66.62,97.45,81.35,85.26,113.4,71.76,70.79,134.4,60.21,89.79,153.5,132.5,92.55,113.4,87.38,78.61,73.93,88.54,129.1,66.72,84.13,84.95,68.01,73.87,138.9,73.28,130.7,113,126.5,91.43,133.6,103.2,110.2,103.7,132.9,111,114.4,100,111.6,135.7,69.28,87.16,82.38,69.5,90.3,72.23,147.3,61.5,115.2,76.2,71.79,120.9,86.24,88.99,126.2,74.24,127.2,108.8,84.08,79.83,77.87,81.89,73.72,72.17,96.03,97.03,83.14,75.54,81.78,88.06,69.14,75,91.22,66.85,129.5,80.43,134.7,66.86,73.59,74.23,84.07,56.36,85.69,82.71,74.33,92.68,82.29,73.73,54.09,79.19,77.25,118.7,60.07,78.6,66.52,131.1,82.82,135.9,78.01,81.25,90.03,76.09,106.9,107.5,105.8,84.52,71.94,71.38,77.88,111.8,84.08,122.9,64.41,155.1,94.15,61.64,71.49,129.9,75.03,66.2,76.66,94.87,73.02,77.23,73.7,107.1,174.2,98,71.24,81.92,85.09,88.52,56.74,59.82,79.42,85.24,81.87,106.6,85.48,133.8,133.7,78.31,140.9,147.2,109,97.65,141.3,134.8,87.84,106.3,70.15,85.89,88.27,73.3,73.16,70.67,78.75,80.64,85.79,93.97,78.78,88.37,73.38,128.9,65.75,55.27,102.4,144.4,78.07,89.75,88.1,83.05,70.31,75.26,124.4,76.14,84.18,83.18,78.29,70.39,104.3,82.63,117.8,78.41,72.49,70.92,59.75,97.53,96.71,76.39,59.6,102.9,80.88,70.95,74.2,98.22,75.46,89.46,61.93,63.19,67.49,68.79,70.47,80.98,102.1,81.47,133.8,123.7,94.89,91.12,82.67,89.78,88.68,89.59,71.73,112.4,88.37,66.82,117.5,77.61,117.3,95.88,94.25,138.1,76.83,127.7,76.77,93.86,80.62,86.34,74.87,84.1,82.61,61.68,111.2,186.9,92.25,73.88,84.28,86.87,85.98,61.06,119,76.38,61.49,76.85,96.45,77.42,70.41,82.89,92.41,88.97,73.99,109.8,78.29,88.73,87.32,87.76,102.8,82.85,94.21,128.1,75.49,107.1,78.18,114.6,118.4,78.83,84.06,96.12,82.69,80.45,121.3,137.8,98.73,92.33,81.25,152.1,61.49,64.12,79.47,71.25,104.7,103.8,76.31,94.66,88.64,94.29,97.26,72.76,120.8,130.5,84.45,82.51,59.96,165.5,71.3,88.73,63,54.53,87.44,78.94,90.31,77.83,75.89,75.21,87.76,134.7,70.79,137.8,93.77,76.37,47.98,48.34,74.65,95.81,94.7,84.88,89.77,87.19,65.31,65.85,61.05,68.89,68.51,71.49,81.35,59.01,82.5,65.67,64.73,59.26,96.39,74.52,91.38,70.67,103.4,143,142,131.2,108.3,140.1,47.92,~
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0.1189,0.08902,0.08758,0.173,0.07678,0.1244,0.08368,0.1151,0.1072,0.2075,0.08452,0.1048,0.1023,0.06287,0.1431,0.1341,0.08216,0.1142,0.07615,0.07259,0.08183,0.07773,0.09946,0.07526,0.09564,0.1059,0.1275,0.07421,0.09876,0.07919,0.09782,0.1402,0.08482,0.1123,0.1233,0.08633,0.1014,0.06169,0.05504,0.1071,0.07146,0.09606,0.1038,0.1027,0.09618,0.09185,0.07409,0.1179,0.08301,0.06917,0.06563,0.08025,0.07408,0.07987,0.07873,0.07036,0.08294,0.1094,0.06289,0.09026,0.0802,0.07712,0.1132,0.0849,0.1031,0.08911,0.09211,0.06641,0.1175,0.0641,0.06589,0.1084,0.1339,0.103,0.07609,0.06387,0.07191,0.1108,0.09964,0.07918,0.08851,0.1016,0.1051,0.09203,0.07924,0.08579,0.06846,0.09288,0.09261,0.08473,0.07246,0.06828,0.06206,0.06603,0.08234,0.08368,0.07376,0.08988,0.08756,0.09353,0.07397,0.09382,0.06878,0.0849,0.07552,0.1405,0.09097,0.07185,0.09789,0.08832,0.08468,0.08486,0.1082,0.09026,0.1017,0.08541,0.07722,0.1065,0.1252,0.06111,0.08523,0.08456,0.08009,0.08006,0.07628,0.07182,0.079,0.06541,0.07779,0.08465,0.09241,0.08019,0.07619,0.07071,0.0761,0.08067,0.07343,0.06765,0.07147,0.06784,0.08151,0.08158,0.08096,0.08118,0.06769,0.1036,0.103,0.09218,0.07683,0.07014,0.06435,0.1486,0.1259,0.06772,0.08633,0.08132,0.07738,0.05972,0.07898,0.07685,0.07987,0.06251,0.09223,0.09082,0.09187,0.06085,0.07699,0.07228,0.093,0.06428,0.06771,0.07371,0.101,0.07313,0.06164,0.07848,0.1162,0.09519,0.05843,0.07319,0.08082,0.1284,0.08631,0.07427,0.09772,0.07697,0.06938,0.07097,0.06576,0.06306,0.1446,0.06871,0.06559,0.1205,0.08701,0.06949,0.09333,0.06558,0.09221,0.1013,0.08174,0.07867,0.08762,0.1086,0.0875,0.0974,0.0738,0.06469,0.1076,0.07474,0.05865,0.07993,0.05525,0.06818,0.1026,0.1059,0.08365,0.07809,0.08255,0.07568,0.08718,0.08177,0.08797,0.1064,0.07623,0.06072,0.08269,0.08362,0.09585,0.1243,0.09061,0.07087,0.07307,0.08328,0.08178,0.07617,0.08677,0.07127,0.07796,0.08496,0.0651,0.06783,0.1297,0.06321,0.07614,0.07748,0.07198,0.1178,0.08147,0.07809,0.07849,0.06487,0.1297,0.08113,0.0895,0.07957,0.1005,0.1191,0.1019,0.1204,0.07999,0.06515,0.07484,0.06829,0.0757,0.08218,0.07587,0.07024,0.07062,0.08701,0.0612,0.08022,0.08858,0.08175,0.07948,0.06033,0.06386,0.05737,0.06263,0.06912,0.0972,0.06688,0.07787,0.1063,0.07127,0.06431,0.09981,0.06915,0.07009,0.06994,0.08799,0.08472,0.09584,0.07007,0.06922,0.06794,0.06643,0.06915,0.07676,0.06777,0.09929,0.07764,0.09469,0.07842,0.07638,0.06745,0.08385,0.07804,0.06192,0.0658,0.06958,0.05695,0.08253,0.07434,0.08116,0.06174,0.06037,0.08198,0.1055,0.05932,0.09702,0.05933,0.08553,0.1024,0.07961,0.06888,0.07083,0.07037,0.082,0.07953,0.09124,0.09166,0.06522,0.07418,0.07207,0.07599,0.1009,0.0987,0.07664,0.07738,0.08764,0.09825,0.0908,0.07918,0.07806,0.08488,0.08083,0.08187,0.08763,0.0759,0.06825,0.105,0.08815,0.09438,0.07018,0.07188,0.08317,0.07113,0.07431,0.08136,0.05521,0.06658,0.07238,0.06469,0.07582,0.06735,0.07632,0.0747,0.06494,0.08574,0.09614,0.06766,0.08666,0.07055,0.07701,0.0896,0.12,0.07061,0.09638,0.1403,0.09215,0.07287,0.09349,0.1118,0.0732,0.06836,0.08824,0.06623,0.1043,0.07602,0.07722,0.08865,0.1019,0.1007,0.07081,0.06609,0.07686,0.07053,0.09158,0.08121,0.1198,0.07262,0.07247,0.07834,0.05974,0.07732,0.07012,0.08151,0.08503,0.06896,0.07745,0.07881,0.09206,0.09251,0.06165,0.07351,0.08304,0.1048,0.09464,0.07123,0.08284,0.09208,0.07427,0.08839,0.08009,0.08061,0.09646,0.07662,0.07083,0.06025,0.1155,0.09359,0.08075,0.08314,0.06827,0.1055,0.07735,0.07234,0.06911,0.0671,0.09532,0.07944,0.0681,0.06736,0.08225,0.08251,0.09075,0.07285,0.07463,0.07425,0.09952,0.06091,0.08194,0.0781,0.0733,0.07675,0.0722,0.06788,0.06291,0.07211,0.0906,0.07427,0.06464,0.07863,0.06925,0.1249,0.08174,0.07875,0.1224,0.0927,0.08524,0.06639,0.08273,0.06743,0.108,0.07802,0.07858,0.07698,0.0918,0.09136,0.07729,0.07603,0.09326,0.0696,0.08181,0.1034,0.06596,0.09009,0.08024,0.07623,0.08203,0.05871,0.07625,0.07028,0.07429,0.06599,0.1033,0.07661,0.09445,0.08999,0.08549,0.1183,0.07538,0.07277,0.1252,0.1364,0.08839,0.1168,0.0723,0.1067,0.09879,0.06142,0.1109,0.07048,0.06954,0.06783,0.07863,0.09136,0.08893,0.08557,0.08982,0.09671,0.07613,0.09031,0.09209,0.1049,0.08665,0.07592,0.07253,0.08052,0.07757,0.0895,0.07782,0.06386,0.08278,0.07569,0.08351,0.0997,0.09938,0.1066,0.08134,0.1023,0.06956,0.06443,0.08492,0.06953,0.07399,0.09479,0.0792,0.07626,0.06592,0.08032,0.06484,0.07393,0.07242,0.08283,0.06742,0.06969,0.08004,0.08732,0.08321,0.05905,0.1409,0.09873,0.07115,0.06637,0.0782,0.124,0.07039)
NodeLocation: 192,104,1
NodeSize: 48,24
WindState: 2,493,559,720,350
ValueState: 2,60,203,1062,586,0,MIDM
ReformVal: [Column,Id]

Constant Pct_training
Title: pct training
Description: Percentage of the data to be used for training.
Definition: 80%
NodeLocation: 696,40,1
NodeSize: 48,24
WindState: 2,499,558,720,350

Index Training_id
Title: Training ID
Description: A subset of the data used for training.
Definition: subset(@ID <= size(ID) * Pct_training )
NodeLocation: 360,40,1
NodeSize: 48,24
WindState: 2,577,569,720,350

Index Testing_id
Title: Testing ID
Description: A subset of the data that is not used for training, which will be used for testing.
Definition: subset(@ID > size(ID) * Pct_training )
NodeLocation: 544,40,1
NodeSize: 48,24
WindState: 2,502,553,720,350

Module Training_on_all_the_
Title: Training on all the data
Description: Performs both Regression and Logistic Regression on the full data set. ~
During this part of the webinar, this demonstrated how to use both functions.
Author: Lonnie
Date: Tue, Sep 18, 2018 5:40 PM
NodeLocation: 192,272,1
NodeSize: 72,40
DiagState: 2,60,399,872,386,17
WindState: 2,542,473,720,350

Variable Logistic_coefficient
Title: Logistic coefficients
Description: The fitted coefficients found by LogisticRegression( ), when fitted to the full data set.
Definition: LogisticRegression( is_malignant, Breast_cancer_data, ID, Column )
NodeLocation: 104,48,1
NodeSize: 48,24
WindState: 2,196,532,720,350
ValueState: 2,23,13,804,900,1,MIDM
GraphSetup: {!50000|Graph_SortBySpread:1}~
Att_GraphValueRange Logistic_coefficient:1,,,,,,10~
Att_GraphIndexRange Column:1,,,,,,10~
Flip:8
ReformVal: [Undefined,Column]

Variable Predicted
Title: predicted
Description: The predictions from Logistic Regression on the same data it was trained on. The color of the points show the true classification, while the Y-axis shows the prediction. When viewed in Analytica 5.0 or higher, the points are sorted by prediction.
Definition: Sigmoid( sum( Breast_cancer_data * Logistic_coefficient, Column ) )
NodeLocation: 232,48,1
NodeSize: 48,24
ValueState: 2,28,376,691,538,1,MIDM
GraphSetup: Graph_HLabelRotation:20~
Att_GraphIndexRange Id:1,,,,,,10~
Att_ContLineStyle Graph_Primary_Valdim:4~
Flip:0~
Att_GraphValueRange Is_malignant:1,,,,,,10~
Att_GraphValueRange Predicted:1,,,,,,10~
Att_CatLineStyle Graph_Primary_Valdim:4~
{!50000|Graph_SortBySpread:1}
ReformVal: [Id,Undefined]
XyExpr: Is_malignant
Att_XRole: Id
Att_ColorRole: 1
{!40600|Att_ClusterIndex: Null}

Variable Regression_coefficie
Title: regression coefficients (on all the data)
Definition: regression(Is_malignant,Breast_cancer_data,ID,Column)
NodeLocation: 104,136,1
NodeSize: 48,40
ValueState: 2,56,58,643,806,1,MIDM
GraphSetup: Att_GraphValueRange Regression_coefficie:1,,,,,,10~
Att_GraphIndexRange Column:1,,,,,,10~
Flip:8~
{!50000|Graph_SortBySpread:1}

Variable Reg_fit
Title: Reg fit
Description: The prediction from straight Regression on the same points in was trained on. The color depicts the true classification.~
~
One thing to notice is how many predictions lie outside of the [0,1] interval. Hence, it is dubious to interpret the output of a regression model as probabilities.
Definition: sum(Regression_coefficie*Breast_cancer_data,column)
NodeLocation: 232,136,1
NodeSize: 48,24
ValueState: 2,365,118,959,526,1,MIDM
GraphSetup: Graph_HLabelRotation:20~
{!50000|Graph_SortBySpread:0}~
Att_CatLineStyle Graph_Primary_Valdim:4~
Att_GraphValueRange Reg_fit:1,,,,,,10~
Att_GraphValueRange Is_malignant:1,,,,,,10~
Flip:0~
Att_ContLineStyle Graph_Primary_Valdim:4
ReformVal: [Undefined,Id]
Att_XRole: Id
Att_ColorRole: 1
Att_ExogenousValues: [Is_malignant]
{!40600|Att_ClusterIndex: Null}

Close Training_on_all_the_

Module Simple_regression_wi
Title: Simple regression with training and test sets
Description: Fits the training data using a linear Regression, and tests accuracy on the test data.~
~
The dependent variable is always 0 or 1 in the training data. Regression does not limit the prediction to land between 0 and 1, you can observe that it lands outside the [0,1] range frequently. To measure its performance by interpreting the prediction as a probability. the predictions are thresholded to 0 or 1.
Author: Lonnie
Date: Tue, Sep 18, 2018 5:40 PM
NodeLocation: 360,272,1
NodeSize: 72,40
DiagState: 2,187,377,700,299,17
WindState: 2,497,463,720,350

Variable C
Title: c -- using Simple Regression
Definition: regression(Training_classificat, Training_data, Training_id, Column,)
NodeLocation: 184,136,1
NodeSize: 48,32
ValueState: 2,516,180,804,732,1,MIDM
GraphSetup: Att_GraphValueRange C:1,,,,,,10~
Att_GraphIndexRange Column:1,,,,,,10~
Flip:8~
{!50000|Graph_SortBySpread:1}

Variable Test_fit
Title: Test fit
Definition: min([ 1, max([0, sum( C*Test_data, Column)])])
NodeLocation: 336,256,1
NodeSize: 48,24
ValueState: 2,644,13,514,394,1,MIDM
GraphSetup: {!40000|Att_graphvaluerange Test_fit:1,,,,,,10}~
{!40000|Att_graphvaluerange Test_classification:1,,,,,,10}~
{!40000|Flip:8}~
{!40000|Att_contlinestyle Graph_primary_valdim:4}
XyExpr: Test_classification

Objective Regression_score
Title: Regression score
Definition: Exp(Average( if Test_classification then ln(Test_fit ) else ln(1-Test_fit), Testing_id ))
NodeLocation: 536,256,1
NodeSize: 48,24
ValueState: 2,665,436,416,210,0,MIDM
NumberFormat: 2,%,4,1,0,0,4,0,$,0,"ABBREV",0,,,0,0,15

Alias Al1537318502
Title: Test classification
Definition: 1
NodeLocation: 528,40,1
NodeSize: 52,24
Original: Test_classification

Alias Al1537318720
Title: Training data
Definition: 1
NodeLocation: 120,40,1
NodeSize: 48,24
Original: Training_data

Alias Al1537318721
Title: Training classifications
Definition: 1
NodeLocation: 256,40,1
NodeSize: 64,24
Original: Training_classificat

Alias Al1537318722
Title: Test data
Definition: 1
NodeLocation: 408,40,1
NodeSize: 48,24
Original: Test_data

Close Simple_regression_wi

Variable Test_classification
Title: Test classification
Description: Classifications for the test set.  For each sample in the test set, this is 1 if that sample is malignant, 0 if benign. These are compared to the model's predictions to measure how well the fitted model performs.
Definition: Is_malignant[ID=Testing_id]
NodeLocation: 544,168,1
NodeSize: 52,24
WindState: 2,563,567,720,350
Aliases: Alias Al1537318502, Alias Al1537319459

Module Logistic_Regression1
Title: Logistic Regression with training and test sets
Description: Trains a Logistic Regression model on the training data, and tests its performance on the test set.~
~
A Logistic Regression model predicts a probability, so its output is readily interpreted as the probability that a new sample is malignant. ~
~
This example uses a maximum likelihood fit.
Author: Lonnie
Date: Tue, Sep 18, 2018 5:40 PM
NodeLocation: 544,272,1
NodeSize: 72,40
DiagState: 2,204,284,944,540,17
WindState: 2,469,553,720,350

Variable Lr_c
Title: lr c
Description: The coefficients found by logistic regression, when trained on the training data.
Definition: LogisticRegression(Training_classificat, Training_data, Training_id, Column)
NodeLocation: 96,240,1
NodeSize: 48,24
ValueState: 2,288,157,878,767,1,MIDM
GraphSetup: {!50000|Graph_SortBySpread:1}~
Att_GraphValueRange Lr_c:1,,,,,,10~
Att_GraphIndexRange Column:1,,,,,,10~
Flip:8

Variable LR_test_fit
Title: LR test fit
Description: The predictions of the logistic regression model on the test data. In the graph, color is used to show the true classification.
Definition: Sigmoid( sum( lr_c*Test_data, Column) )
NodeLocation: 248,240,1
NodeSize: 48,24
ValueState: 2,600,484,703,456,1,MIDM
GraphSetup: {!50000|Graph_SortBySpread:1}~
Att_CatLineStyle Graph_Primary_Valdim:4~
Graph_HLabelRotation:20~
Flip:0~
Att_GraphValueRange Test_classification:1,,,,,,10~
Att_GraphValueRange LR_test_fit:1,,,,,,10~
Att_ContLineStyle Graph_Primary_Valdim:4~
Att_GraphIndexRange Testing_id:1,,,,,,10
ReformVal: [Testing_id,Undefined]
XyExpr: Test_classification
Att_XRole: Testing_id
Att_ColorRole: 1
{!40600|Att_ClusterIndex: Null}

Objective Lr_score
Title: LR score
Description: The score here can be interpreted as an average accuracy -- something along the lines of this. If it says 93%, then on a new datum where the classification is 1, it would average a prediction of 93%, or on a datum with classification of 0 it would average a prediction of 0. The average here is not an arithmetic average, but is an average more suited for probabilistic predictions like this.
Definition: Exp(Average( if Test_classification then ln(LR_test_fit ) else ln(1-LR_test_fit), Testing_id ))
NodeLocation: 392,240,1
NodeSize: 48,24
WindState: 2,102,82,1003,491
ValueState: 2,113,389,796,517,1,MIDM
GraphSetup: Att_GraphValueRange Lr_score:1,,0,,1,,,0.9,0.95,0
NumberFormat: 2,%,4,1,0,0,4,0,$,0,"ABBREV",0,,,0,0,15

Variable LR_train_fit
Title: LR train fit
Description: The logistic regression model's predictions on the data in was trained on.
Definition: Sigmoid( sum( lr_c*Training_data, Column) )
NodeLocation: 96,352,1
NodeSize: 48,24
ValueState: 2,125,83,765,493,1,MIDM
GraphSetup: Graph_HLabelRotation:20~
Att_CatLineStyle Graph_Primary_Valdim:4~
{!50000|Graph_SortBySpread:1}
ReformVal: [Training_id,Undefined]
XyExpr: Training_classificat
Att_XRole: Training_id
Att_ColorRole: 1

Alias Al1537319456
Title: Training data
Definition: 1
NodeLocation: 96,80,1
NodeSize: 48,24
Original: Training_data

Alias Al1537319457
Title: Training classifications
Definition: 1
NodeLocation: 96,144,1
NodeSize: 64,24
Original: Training_classificat

Alias Al1537319458
Title: Test data
Definition: 1
NodeLocation: 248,80,1
NodeSize: 48,24
Original: Test_data

Alias Al1537319459
Title: Test classification
Definition: 1
NodeLocation: 248,152,1
NodeSize: 52,24
Original: Test_classification

Variable LR_Train_score
Title: LR Train score
Description: Accuracy of the Logistic Regression model on the training data. We don't really care about how it does on the training data, but it can be interesting to compare to how it does on the testing data. (It will always do better on the training data).
Definition: Exp(Average( if Training_classificat then ln(LR_train_fit ) else ln(1-LR_train_fit), Training_id ))
NodeLocation: 96,424,1
NodeSize: 48,24
ValueState: 2,137,597,416,303,,MIDM
NumberFormat: 2,%,4,1,0,0,4,0,$,0,"ABBREV",0,,,0,0,15

Close Logistic_Regression1

Variable Training_data
Title: Training data
Description: A subset of the full data to be used for training.
Definition: Breast_cancer_data[ID=Training_id]
NodeLocation: 360,104,1
NodeSize: 48,24
WindState: 2,590,594,720,350
Aliases: Alias Al1537318720, Alias Al1537319456

Variable Training_classificat
Title: Training classifications
Description: Whether each sample in the training set is malignant (1) or benign (0).
Definition: Is_malignant[ID=Training_id]
NodeLocation: 360,168,1
NodeSize: 64,24
WindState: 2,497,587,720,350
Aliases: Alias Al1537318721, Alias Al1537319457

Variable Test_data
Title: Test data
Description: A subset of the full data, not used for training, to be used to measure how well the fitted model generalizes.
Definition: Breast_cancer_data[ID=Testing_id]
NodeLocation: 544,104,1
NodeSize: 48,24
WindState: 2,528,543,720,350
Aliases: Alias Al1537318722, Alias Al1537319458

Objective Compare_accuracies
Title: Compare accuracies
Definition: Table(Self)(Regression_score,LR_Score)
IndexVals: ['Regression','LogisticRegression']
NodeLocation: 456,392,1
NodeSize: 48,24
ValueState: 2,885,36,374,225,,MIDM
NumberFormat: 2,%,4,1,0,0,4,0,$,0,"ABBREV",0,,,0,0,15
Att_PrevIndexValue: ['Regression','LogisticRegression']

Close Logistic_Regression_