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Decision_Trees_Guide.m
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Decision_Trees_Guide.m
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% Decision Tree Algorithm
%---------------Importing Dataset
data = readtable('Datasets\Social_Network_Ads.csv');
%---------------Feature Scaling (Standardization Method)
stand_age = (data.Age - mean(data.Age))/std(data.Age);
data.Age = stand_age;
stand_estimted_salary = (data.EstimatedSalary - mean(data.EstimatedSalary))/std(data.EstimatedSalary);
data.EstimatedSalary = stand_estimted_salary;
%---------------Classifying Data
classification_model = fitctree(data,'Purchased~Age+EstimatedSalary');
%Max number of branch nodes / splits
%classification_model = fitctree(data, 'Purchased~Age+EstimatedSalary','MaxNumSplits',7);
%Min size of parent
%classification_model = fitctree(data, 'Purchased~Age+EstimatedSalary','MinParentSize',20);
%classification_model = fitctree(data, 'Purchased~Age+EstimatedSalary','SplitCriterion','gdi');
%classification_model = fitctree(data, 'Purchased~Age+EstimatedSalary','SplitCriterion','twoing');
%classification_model = fitctree(data, 'Purchased~Age+EstimatedSalary','SplitCriterion','deviance');
%---------------Partitioning
cv = cvpartition(classification_model.NumObservations, 'HoldOut', 0.2);
cross_validated_model = crossval(classification_model,'cvpartition',cv);
%---------------Predictions
Predictions = predict(cross_validated_model.Trained{1},data(test(cv),1:end-1));
%---------------Analyzing the Results
Results = confusionmat(cross_validated_model.Y(test(cv)),Predictions);
%---------------Visualizing Training Results
labels = unique(data.Purchased);
classifier_name = 'Decision Tree(Training Results)';
Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;
[xx1, xx2] = meshgrid(Age_range,Estimated_salary_range);
XGrid = [xx1(:) xx2(:)];
predictions_meshgrid = predict(cross_validated_model.Trained{1},XGrid);
gscatter(xx1(:), xx2(:), predictions_meshgrid,'rgb');
hold on
training_data = data(training(cv),:);
Y = ismember(training_data.Purchased,labels{1});
scatter(training_data.Age(Y),training_data.EstimatedSalary(Y), 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'red');
scatter(training_data.Age(~Y),training_data.EstimatedSalary(~Y) , 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'green');
xlabel('Age');
ylabel('Estimated Salary');
title(classifier_name);
legend off, axis tight
legend(labels,'Location',[0.45,0.01,0.45,0.05],'Orientation','Horizontal');
%---------------Creating View
%view(cross_validated_model.Trained{1}) %if statements for tree in console
view(cross_validated_model.Trained{1}, 'Mode', 'Graph');
%---------------Visualizing Test Results
labels = unique(data.Purchased);
classifier_name = 'Decision Tree (Testing Results)';
Age_range = min(data.Age(training(cv)))-1:0.01:max(data.Age(training(cv)))+1;
Estimated_salary_range = min(data.EstimatedSalary(training(cv)))-1:0.01:max(data.EstimatedSalary(training(cv)))+1;
[xx1, xx2] = meshgrid(Age_range,Estimated_salary_range);
XGrid = [xx1(:) xx2(:)];
predictions_meshgrid = predict(cross_validated_model.Trained{1},XGrid);
figure
gscatter(xx1(:), xx2(:), predictions_meshgrid,'rgb');
hold on
testing_data = data(test(cv),:);
Y = ismember(testing_data.Purchased,labels{1});
scatter(testing_data.Age(Y),testing_data.EstimatedSalary(Y), 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'red');
scatter(testing_data.Age(~Y),testing_data.EstimatedSalary(~Y) , 'o' , 'MarkerEdgeColor', 'black', 'MarkerFaceColor', 'green');
xlabel('Age');
ylabel('Estimated Salary');
title(classifier_name);
legend off, axis tight
legend(labels,'Location',[0.45,0.01,0.45,0.05],'Orientation','Horizontal');