Alternate way for KFold

Kfold_Validation.asv

@@ -0,0 +1,66 @@
+%---------------------K-Nearest Neighbour Algorithm------------------------
+
+% 1- Choose the number of k neighbours.
+
+% 2- Compute the k neighbors of the new data point according to some
+% distance measure such as Euclidean.
+
+% 3- Count the number of data points from each category among the
+% neighbours computed in step 2.
+
+% 4- The new datapoint is assigned to the category whit most neighbours.
+
+data = readtable('Datasets\Social_Network_Ads.csv');
+
+%--------Preprocess
+sum(ismissing(data)); % Count of missing values in columns
+
+% Check for the outliers with plotting
+% plot(data.Age);
+% plot(data.EstimatedSalary)
+
+% Feature Scaling with Standardization
+stand_age = (data.Age - mean(data.Age)) / std(data.Age);
+data.Age = stand_age;
+
+stand_estimatedsalary = (data.EstimatedSalary - mean(data.EstimatedSalary)) / std(data.EstimatedSalary);
+data.EstimatedSalary = stand_estimatedsalary;
+
+%-----------Classifying data
+classification_model = fitcknn(data, 'Purchased~Age+EstimatedSalary'); %Classification Model
+classification_model.NumNeighbors = 5; %Change number of neighbours.
+%classification_model.NumNeighbors = 3; % Change the neighbor number to get better result
+%classification_model = fitcknn(standardized_data, 'Survived~Age+Fare+Parch+SibSp+female+male+Pclass','NumNeighbors', 3);
+%classification_model = fitcknn(standardized_data, 'Survived~Age+Fare+Parch+SibSp+female+male+Pclass','Distance', 'seuclidean');
+
+
+%-----------Divide data into training and testing sets
+%Numberof observations, classification models, percentage. Randomly choose
+%0.2 percentage for testing.
+
+%Validation with Holdout and Kfold
+%cv = cvpartition(classification_model.NumObservations,'HoldOut', 0.2); 
+
+cv = cvpartition(classification_model.NumObservations,'KFold', 5); %Produce 5 classifiers
+cross_validated_model = crossval(classification_model, 'cvpartition', cv); %Use training set only to built model 
+
+%-----------Make predictions for the each testing set
+Predictions_K1 = predict(cross_validated_model.Trained{1}, data(test(cv,1),1:end-1));
+Predictions_K2 = predict(cross_validated_model.Trained{1}, data(test(cv,2),1:end-1));
+Predictions_K3 = predict(cross_validated_model.Trained{1}, data(test(cv,3),1:end-1));
+Predictions_K4 = predict(cross_validated_model.Trained{1}, data(test(cv,4),1:end-1));
+Predictions_K5 = predict(cross_validated_model.Trained{1}, data(test(cv,5),1:end-1));
+
+%Alternative Way
+Predictions = kfoldPredict(cross_validated_model); .5
+%-----------Analyzing the predictions
+    %Confusion Matrix for each test results
+Results_K1 = confusionmat(cross_validated_model.Y(test(cv,1)),Predictions_K1); 
+Results_K2 = confusionmat(cross_validated_model.Y(test(cv,2)),Predictions_K2); 
+Results_K3 = confusionmat(cross_validated_model.Y(test(cv,3)),Predictions_K3); 
+Results_K4 = confusionmat(cross_validated_model.Y(test(cv,4)),Predictions_K4); 
+Results_K5 = confusionmat(cross_validated_model.Y(test(cv,5)),Predictions_K5); 
+%data.Purchased(test(cv));
+
+%Combine results in  one confusion matrix
+Results = Results_K1 + Results_K2 + Results_K3 + Results_K4 + Results_K5

Kfold_Validation.m

@@ -39,16 +39,20 @@
 %0.2 percentage for testing.
 
 %Validation with Holdout and Kfold
-%cv = cvpartition(classification_model.NumObservations,'HoldOut', 0.2); %Built-in function for partitioning
+%cv = cvpartition(classification_model.NumObservations,'HoldOut', 0.2); 
+
 cv = cvpartition(classification_model.NumObservations,'KFold', 5); %Produce 5 classifiers
 cross_validated_model = crossval(classification_model, 'cvpartition', cv); %Use training set only to built model 
 
 %-----------Make predictions for the each testing set
 Predictions_K1 = predict(cross_validated_model.Trained{1}, data(test(cv,1),1:end-1));
-Predictions_K2 = predict(cross_validated_model.Trained{1}, data(test(cv,2),1:end-1));
-Predictions_K3 = predict(cross_validated_model.Trained{1}, data(test(cv,3),1:end-1));
-Predictions_K4 = predict(cross_validated_model.Trained{1}, data(test(cv,4),1:end-1));
-Predictions_K5 = predict(cross_validated_model.Trained{1}, data(test(cv,5),1:end-1));
+Predictions_K2 = predict(cross_validated_model.Trained{2}, data(test(cv,2),1:end-1));
+Predictions_K3 = predict(cross_validated_model.Trained{3}, data(test(cv,3),1:end-1));
+Predictions_K4 = predict(cross_validated_model.Trained{4}, data(test(cv,4),1:end-1));
+Predictions_K5 = predict(cross_validated_model.Trained{5}, data(test(cv,5),1:end-1));
+
+%Alternative Way (Does not display individual results, which result belongs to which line)
+Predictions = kfoldPredict(cross_validated_model); %Make predictions for 5 experiments
 
 %-----------Analyzing the predictions
     %Confusion Matrix for each test results
@@ -57,7 +61,10 @@
 Results_K3 = confusionmat(cross_validated_model.Y(test(cv,3)),Predictions_K3); 
 Results_K4 = confusionmat(cross_validated_model.Y(test(cv,4)),Predictions_K4); 
 Results_K5 = confusionmat(cross_validated_model.Y(test(cv,5)),Predictions_K5); 
-%data.Purchased(test(cv));
+% %data.Purchased(test(cv));
+
+% %Combine results in  one confusion matrix
+Results_K = Results_K1 + Results_K2 + Results_K3 + Results_K4 + Results_K5;
 
-%Combine results in  one confusion matrix
-Results = Results_K1 + Results_K2 + Results_K3 + Results_K4 + Results_K5
+%Alternate Way
+Results = confusionmat(table2cell(data(:,end)),Predictions);