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‎Machine_Learning_On_Breast_Cancer_Dataset/ML_Report.pdf‎

164 KB
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‎Machine_Learning_On_Breast_Cancer_Dataset/data/bwc_data.xlsx‎

27.5 KB
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‎Machine_Learning_On_Breast_Cancer_Dataset/metrics/BAYES_stats‎

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In every of the above i used :
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mdl.Prior = struct('ClassNames',[2, 4],'ClassProbs',[0.655; 0.345]);
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Type your choice : BAYES
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mdl = fitcnb(X, Y);
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costs : 0.1 , 0.9
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.756419 secs
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|Total True Positive = 237
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|Total True Negative = 435
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|Total False Positive = 23
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|Total False Negative = 4
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|Accuracy = 96.137%
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|Sensitivity = 98.340%
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|Specificity = 94.978%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y);
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costs : 0.25 , 0.75
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.723786 secs
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|Total True Positive = 237
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|Total True Negative = 436
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|Total False Positive = 22
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|Total False Negative = 4
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|Accuracy = 96.280%
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|Sensitivity = 98.340%
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|Specificity = 95.197%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y);
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costs : 0.2 , 0.8
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.860953 secs
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|Total True Positive = 236
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|Total True Negative = 436
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|Total False Positive = 22
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|Total False Negative = 5
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|Accuracy = 96.137%
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|Sensitivity = 97.925%
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|Specificity = 95.197%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y,'DistributionNames','kernel','Kernel','normal');
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costs : 0.25 , 0.75
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 1.238566 secs
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|Total True Positive = 232
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|Total True Negative = 443
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|Total False Positive = 15
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|Total False Negative = 9
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|Accuracy = 96.567%
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|Sensitivity = 96.266%
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|Specificity = 96.725%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y,'DistributionNames','kernel', 'Kernel', 'box');
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costs : 0.25 , 0.75
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 1.302383 secs
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|Total True Positive = 233
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|Total True Negative = 443
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|Total False Positive = 15
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|Total False Negative = 8
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|Accuracy = 96.710%
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|Sensitivity = 96.680%
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|Specificity = 96.725%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y,'DistributionNames','kernel', 'Kernel', 'triangle');
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costs : 0.25 , 0.75
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 1.169238 secs
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|Total True Positive = 232
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|Total True Negative = 445
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|Total False Positive = 13
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|Total False Negative = 9
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|Accuracy = 96.853%
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|Sensitivity = 96.266%
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|Specificity = 97.162%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y,'CategoricalPredictors', 'all', 'DistributionNames', 'mvmn');
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costs : 0.25 , 0.75
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.792678 secs
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|Total True Positive = 239
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|Total True Negative = 443
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|Total False Positive = 15
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|Total False Negative = 2
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|Accuracy = 97.568%
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|Sensitivity = 99.170%
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|Specificity = 96.725%
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------------------------------------------
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Type your choice : BAYES
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mdl = fitcnb(X, Y,'DistributionNames','normal');
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.800770 secs
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|Total True Positive = 235
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|Total True Negative = 436
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|Total False Positive = 22
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|Total False Negative = 6
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|Accuracy = 95.994%
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|Sensitivity = 97.510%
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|Specificity = 95.197%
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------------------------------------------

‎Machine_Learning_On_Breast_Cancer_Dataset/metrics/DTree‎

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Type your choice : DTree
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.091613 secs
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|Total True Positive = 233
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|Total True Negative = 432
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|Total False Positive = 26
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|Total False Negative = 8
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|Accuracy = 95.136%
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|Sensitivity = 96.680%
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|Specificity = 94.323%
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------------------------------------------
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Type your choice : DTree
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mdl = fitctree(X, Y, 'Cost', [0 0.25; 0.75 0], 'AlgorithmForCategorical','Exact');
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.095682 secs
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|Total True Positive = 229
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|Total True Negative = 428
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|Total False Positive = 30
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|Total False Negative = 12
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|Accuracy = 93.991%
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|Sensitivity = 95.021%
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|Specificity = 93.450%
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------------------------------------------
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Type your choice : DTree
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mdl = fitctree(X, Y, 'Cost', [0 0.25; 0.75 0], 'AlgorithmForCategorical','PullLeft');
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.093547 secs
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|Total True Positive = 231
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|Total True Negative = 434
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|Total False Positive = 24
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|Total False Negative = 10
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|Accuracy = 95.136%
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|Sensitivity = 95.851%
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|Specificity = 94.760%
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------------------------------------------
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Type your choice : DTree
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mdl = fitctree(X, Y, 'Cost', [0 0.25; 0.75 0], 'AlgorithmForCategorical','PCA');
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.093534 secs
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|Total True Positive = 227
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|Total True Negative = 434
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|Total False Positive = 24
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|Total False Negative = 14
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|Accuracy = 94.564%
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|Sensitivity = 94.191%
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|Specificity = 94.760%
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------------------------------------------
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Type your choice : DTree
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mdl = fitctree(X, Y, 'Cost', [0 0.25; 0.75 0], 'AlgorithmForCategorical','OVAbyClass');
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.097533 secs
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|Total True Positive = 228
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|Total True Negative = 434
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|Total False Positive = 24
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|Total False Negative = 13
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|Accuracy = 94.707%
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|Sensitivity = 94.606%
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|Specificity = 94.760%
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------------------------------------------

‎Machine_Learning_On_Breast_Cancer_Dataset/metrics/KNN_stats‎

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3 = ceil(log(699))
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 232
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|Total True Negative = 445
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|Total False Positive = 13
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|Total False Negative = 9
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|Accuracy = 9.685265e-01
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|Sensitivity = 9.626556e-01
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|Specificity = 9.716157e-01
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------------------------------------------
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5
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 234
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|Total True Negative = 446
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|Total False Positive = 12
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|Total False Negative = 7
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|Accuracy = 9.728183e-01
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|Sensitivity = 9.709544e-01
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|Specificity = 9.737991e-01
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------------------------------------------
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10
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 229
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|Total True Negative = 446
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|Total False Positive = 12
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|Total False Negative = 12
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|Accuracy = 9.656652e-01
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|Sensitivity = 9.502075e-01
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|Specificity = 9.737991e-01
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------------------------------------------
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20
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 226
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|Total True Negative = 448
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|Total False Positive = 10
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|Total False Negative = 15
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|Accuracy = 9.642346e-01
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|Sensitivity = 9.377593e-01
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|Specificity = 9.781659e-01
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------------------------------------------
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26 = sqrt(699)
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 225
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|Total True Negative = 448
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|Total False Positive = 10
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|Total False Negative = 16
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|Accuracy = 9.628040e-01
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|Sensitivity = 9.336100e-01
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|Specificity = 9.781659e-01
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------------------------------------------
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 224
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|Total True Negative = 447
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|Total False Positive = 11
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|Total False Negative = 17
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|Accuracy = 9.599428e-01
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|Sensitivity = 9.294606e-01
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|Specificity = 9.759825e-01
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------------------------------------------
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total True Positive = 222
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|Total True Negative = 448
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|Total False Positive = 10
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|Total False Negative = 19
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|Accuracy = 9.585122e-01
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|Sensitivity = 9.211618e-01
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|Specificity = 9.781659e-01
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------------------------------------------

‎Machine_Learning_On_Breast_Cancer_Dataset/metrics/KNN_stats_minkow‎

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Used minkowski distance
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Choose K for KNN : 5
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.087527 secs
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|Total True Positive = 234
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|Total True Negative = 447
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|Total False Positive = 11
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|Total False Negative = 7
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|Accuracy = 97.425%
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|Sensitivity = 97.095%
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|Specificity = 97.598%
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------------------------------------------
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Choose K for KNN : 7
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.091131 secs
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|Total True Positive = 229
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|Total True Negative = 447
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|Total False Positive = 11
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|Total False Negative = 12
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|Accuracy = 96.710%
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|Sensitivity = 95.021%
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|Specificity = 97.598%
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------------------------------------------
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Choose K for KNN : 10
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.088568 secs
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|Total True Positive = 229
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|Total True Negative = 447
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|Total False Positive = 11
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|Total False Negative = 12
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|Accuracy = 96.710%
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|Sensitivity = 95.021%
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|Specificity = 97.598%
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------------------------------------------
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Choose K for KNN : 13
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-----------------------------------------
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|Total Predicted = 699, K-fold with K = 10
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|Total time for K-fold = 0.091935 secs
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|Total True Positive = 231
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|Total True Negative = 447
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|Total False Positive = 11
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|Total False Negative = 10
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|Accuracy = 96.996%
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|Sensitivity = 95.851%
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|Specificity = 97.598%
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------------------------------------------

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