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system 10.5 of 04/10/2024,
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FORESIGHT
Probabilities: Array;
The Probabilities property returns a series of forecast probabilities of logistic regression.
To determine threshold value of probability for classification, use the ISmLogisticRegression.Threshold property.
To execute the example, add a link to the Stat system assembly.
Sub UserLogisticR;
Var
binary: SmLogisticRegression;
e, s: Array[62] Of Double;
Coef: IModelCoefficients;
stat: ISummaryStatistics;
i, j, res: Integer;
ROCcurve: IROCcurve;
FilledDependent, ar, Probabilities, ProbFitted: Array Of Double;
OneMinusSpecificity, Sensitivity, CutOffPoints: Array Of Double;
str: String;
Begin
// Set data for analysis
// Values corresponding to social status
s[0] := Double.Nan; s[11] := 0; s[22] := 0; s[33] := Double.Nan; s[44] := 1; s[55] := Double.Nan;
s[1] := 1; s[12] := 1; s[23] := 1; s[34] := 0; s[45] := 1; s[56] := 1;
s[2] := Double.Nan; s[13] := 1; s[24] := 0; s[35] := 0; s[46] := 1; s[57] := 1;
s[3] := 0; s[14] := 0; s[25] := 0; s[36] := 1; s[47] := 0; s[58] := 1;
s[4] := 0; s[15] := 0; s[26] := 0; s[37] := 0; s[48] := 1; s[59] := 1;
s[5] := 1; s[16] := 0; s[27] := 0; s[38] := 0; s[49] := 0; s[60] := 0;
s[6] := 0; s[17] := 0; s[28] := 0; s[39] := 0; s[50] := 0; s[61] := 0;
s[7] := 1; s[18] := Double.Nan; s[29] := Double.Nan; s[40] := 0; s[51] := Double.Nan;
s[8] := 1; s[19] := 0; s[30] := 0; s[41] := 0; s[52] := 0;
s[9] := 1; s[20] := 1; s[31] := 0; s[42] := Double.Nan; s[53] := 0;
s[10] := 1; s[21] := 0; s[32] := 0; s[43] := 1; s[54] := 0;
// Values corresponding to age group
e[0] := 1; e[11] := 0; e[22] := 1; e[33] := 2; e[44] := 2; e[55] := 2;
e[1] := 1; e[12] := 1; e[23] := 2; e[34] := 1; e[45] := 0; e[56] := 0;
e[2] := 1; e[13] := 1; e[24] := 1; e[35] := 3; e[46] := 1; e[57] := 1;
e[3] := 0; e[14] := 1; e[25] := 0; e[36] := 1; e[47] := 1; e[58] := 0;
e[4] := 0; e[15] := 2; e[26] := 1; e[37] := 1; e[48] := 1; e[59] := 0;
e[5] := 1; e[16] := 1; e[27] := 0; e[38] := 2; e[49] := 0; e[60] := 2;
e[6] := 2; e[17] := 0; e[28] := 1; e[39] := 3; e[50] := 1; e[61] := 2;
e[7] := 0; e[18] := 1; e[29] := 3; e[40] := 1; e[51] := 0;
e[8] := 3; e[19] := 3; e[30] := 1; e[41] := 0; e[52] := 0;
e[9] := 1; e[20] := 4; e[31] := 1; e[42] := 4; e[53] := 2;
e[10] := 2; e[21] := 0; e[32] := 2; e[43] := 1; e[54] := 0;
// Create a method
binary := New SmLogisticRegression.Create;
// Set explained series
binary.Dependent.Value := s;
// Set classification attribute
binary.Explanatories.Add.Value := e;
// Set accuracy of solution
binary.Tolerance := 0.005;
// Set maximum number of iterations
binary.MaxIteration := 10000;
// Set ROC curve parameters
ROCcurve := binary.ROCcurve;
ROCcurve.ConfidenceLevel := 0.85;
// Execute calculation and display calculation results
res := binary.Execute;
If res <> 0 Then
Debug.WriteLine(binary.Errors);
Else
Debug.WriteLine("Number of iterations required for solution: ");
Debug.Indent;
Debug.WriteLine(binary.NumOfIter);
Debug.Unindent;
Debug.WriteLine("Before filling; after filling");
Debug.Indent;
FilledDependent := binary.FilledDependent.Value;
For i := 0 To s.Length - 1 Do
If s[i] = 0 Then
Debug.Write("Single ");
Elseif s[i] = 1 Then
Debug.Write("Married ");
Else
Debug.Write(" - ");
End If;
If FilledDependent[i] = 0 Then
Debug.WriteLine("Single");
Else
Debug.WriteLine("Married");
End If;
End For;
Debug.Unindent;
Debug.WriteLine("Estimated coefficient values:");
Debug.Indent;
Coef := binary.ModelCoefficients;
ar := Coef.Coefficients.Estimate;
For i := 0 To ar.Length - 1 Do
Debug.WriteLine(ar[i]);
End For;
Debug.Unindent;
Debug.WriteLine("Mean of log-likelihood function: ");
Debug.Indent;
Stat := binary.SummaryStatistics;
Debug.WriteLine(Stat.AvgLogL);
Debug.Unindent;
Debug.WriteLine("Probability series");
Debug.Indent;
Probabilities := binary.Probabilities;
For i := 0 To Probabilities.Length - 1 Do
Debug.WriteLine((i + 1).ToString + ". " + Probabilities[i].ToString);
End For;
Debug.Unindent;
Debug.WriteLine("Probability series for training objects");
Debug.Indent;
ProbFitted := binary.ProbFitted;
For i := 0 To ProbFitted.Length - 1 Do
Debug.WriteLine((i + 1).ToString + ". " + ProbFitted[i].ToString);
End For;
Debug.Unindent;
// Display ROC curve data
Debug.WriteLine("ROC curve data:");
Debug.Indent;
Debug.WriteLine("Specificity:");
Debug.Indent;
OneMinusSpecificity := ROCcurve.OneMinusSpecificity;
For i := 0 To OneMinusSpecificity.Length - 1 Do
Debug.WriteLine(OneMinusSpecificity[i]);
End For;
Debug.Unindent;
Debug.WriteLine("Sensitivity:");
Debug.Indent;
Sensitivity := ROCcurve.Sensitivity;
For i := 0 To Sensitivity.Length - 1 Do
Debug.WriteLine(Sensitivity[i]);
End For;
Debug.Unindent;
Debug.WriteLine("Area under curve: " + ROCcurve.Area.ToString);
Debug.WriteLine("Standard error: " + ROCcurve.StdError.ToString);
Debug.WriteLine("Asymptotic confidence interval:");
Debug.Indent;
Debug.WriteLine("Lower limit: " + ROCcurve.ConfidenceIntervalLower.ToString);
Debug.WriteLine("Upper limit: " + ROCcurve.ConfidenceIntervalUpper.ToString);
Debug.Unindent;
Debug.WriteLine("Cutoff threshold:");
Debug.Indent;
CutOffPoints := ROCcurve.CutOffPoints;
For i := 0 To CutOffPoints.Length - 1 Do
Debug.WriteLine(CutOffPoints[i]);
End For;
Debug.Unindent;
Debug.Unindent;
// Output summary results of classification
Debug.WriteLine("Summary results of classification:");
Debug.Indent;
str := "";
For i := 0 To binary.ClassificationSummary.GetUpperBound(1) Do
For j := 0 To binary.ClassificationSummary.GetUpperBound(2) Do
str := str + binary.ClassificationSummary[i, j].ToString + " ";
End For;
Debug.WriteLine(str);
str := "";
End For;
Debug.Unindent;
// Display classification quality criteria
Debug.WriteLine("Classification quality criteria");
Debug.Indent;
Debug.WriteLine("Overall accuracy: " + binary.RelevanceMeasure.Accuracy.ToString);
Debug.WriteLine("F - estimate: " + binary.RelevanceMeasure.F1.ToString);
Debug.WriteLine("Positive result accuracy: " + binary.RelevanceMeasure.Precision.ToString);
Debug.WriteLine("Completeness of positive result: " + binary.RelevanceMeasure.Recall.ToString);
Debug.Unindent;
Debug.Unindent;
End If;
End Sub UserLogisticR;
After executing the example, logistic regression is set up and calculated, the console window displays calculation results and classification quality criteria.
See also: