Learning Log
Week 9 – 15th November – 19th November
Practical
Task 1
Task 2
Task 3.1
Supervised learning is where the machine concludes a function from supervised training data. The
training data will consist of training examples. Each example will be a pair consisting of input objesct
and output values. The supervised algorithm will analysis the training data and will produce an
inferred function or classifier.
Task 3.2
=== Run information ===
Scheme:
weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: WDBC-weka.filters.unsupervised.attribute.ReorderR2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,1weka.filters.supervised.attribute.AddClassification-Wweka.classifiers.rules.ZeroRweka.filters.supervised.attribute.AddClassification-Wweka.classifiers.rules.ZeroRweka.filters.supervised.attribute.AddClassification-Wweka.classifiers.rules.ZeroRweka.filters.supervised.attribute.AddClassification-Wweka.classifiers.rules.ZeroRweka.filters.AllFilter
Instances: 569
Attributes: 31
radius1
texture1
perimeter1
area1
smoothness1
compactness1
concavity1
concave1
symmetry1
fractal_dimension1
radius2
texture2
perimeter2
area2
smoothness2
compactness2
concavity2
concave2
symmetry2
fractal_dimension2
radius3
texture3
perimeter3
area3
smoothness3
compactness3
concavity3
concave3
symmetry3
fractal_dimension3
class
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
J48 pruned tree
------------------
area3 <= 880.8
| concave3 <= 0.1357
| | area2 <= 36.46: B (319.0/3.0)
| | area2 > 36.46
| | | radius1 <= 14.97
| | | | texture2 <= 1.978: B (11.0)
| | | | texture2 > 1.978
| | | | | texture2 <= 2.239: M (2.0)
| | | | | texture2 > 2.239: B (3.0)
| | | radius1 > 14.97: M (2.0)
| concave3 > 0.1357
| | texture3 <= 27.37
| | | concave3 <= 0.1789
| | | | area2 <= 21.91: B (12.0)
| | | | area2 > 21.91
| | | | | perimeter2 <= 2.615: M (6.0/1.0)
| | | | | perimeter2 > 2.615: B (6.0)
| | | concave3 > 0.1789: M (4.0)
| | texture3 > 27.37: M (21.0)
area3 > 880.8
| concavity1 <= 0.0716
| | texture1 <= 19.54: B (9.0/1.0)
| | texture1 > 19.54: M (10.0)
| concavity1 > 0.0716: M (164.0)
Number of Leaves :
13
Size of the tree :
25
Time taken to build model: 0.06 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances
530
93.1459 %
39
6.8541 %
Incorrectly Classified Instances
Kappa statistic
0.8544
Mean absolute error
0.0741
Root mean squared error
0.2579
Relative absolute error
15.8366 %
Root relative squared error
53.331 %
Total Number of Instances
569
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.925
0.064
0.895
0.925
0.91
0.927 M
0.936
0.075
0.954
0.936
0.945
0.927 B
Weighted Avg. 0.931
0.071
=== Confusion Matrix ===
a b <-- classified as
196 16 | a = M
23 334 | b = B
0.932
0.931
0.932
0.927
Task 3.3
Sensitivity – 0.931
Specificity – 0.071
Task 3.4
Random Forest Classification –
Correct Classification – 95.7821%
Incorrect Classification – 4.2179%
Tp – 0.958
Kappa – 0.91
Decision Table Classification Correct Classification – 94.0246%
Incorrect Classification – 5.9754%
Tp – 0.94
Kappa – 0.871
JRIP Classification –
Correct Classification – 92.7944%
Incorrect Classification – 7.2056%
Tp – 0.928
Kappa – 0.846
Task 3.5
In terms of correct classification random tree classification is the best with 95.7821%
In terms of TP random forest classification had the highest of 0.958
In terms of kappa random forest had the highest with 0.91.
From the tree classifications above the method in which provided the best results was Radom Forest
Classification.
Task 3.6
Results from increasing the FOLD
10 fold
20 fold
30 fold
40 fold
50 fold
Correct
Classification
95.7821%
93.3216%
94.9033%
94.0246%
95.2548%
Incorrect
Classification
4.2179%
6.6784%
5.0967%
5.9754%
4.7452%
TP
Kappa
0.958
0.933
0.949
0.94
0.953
0.91
0.8577
0.8905
0.8719
0.8982
From the changing the fold from 10 up to 40 the results where worse, 10 fold provided the best
classification. When I entered 50 fold the results appeared to start improving, to see if the higher the
fold was the better the result is I decided to enter a fold of 100, below are the results
100 fold
Correct
Classification
95.4306%
Incorrect
Classification
4.5694%
As you can see the results slightly improved.
TP
Kappa
0.954
0.9011
Learning Log
Week 10 – 22nd November – 26th November
Practical
Task 2.1
Unsupervised learning is a class of problems where you seek to determine how the data is organised.
There are many methods employed here which are based on data mining methods used to preprocess data. It is different from supervised learning as the learner is only given unlabelled
examples.
Task 2.3
I expect to see two clusters from the dataset
Task 2.5
Sensitivity = 0.08421
Specificity = 0.04761
Task 2.6
EM -1
Using EM-1 did not cluster the data correctly.
EM -2
Sensitivity = 0.5507
Specificity = 0.1383
Tasks 3.1
Data cleansing is where the detection and correction or removal of corrupt or inaccurate records
from the record set takes place.
Task 3.2
Data cleansing algorithms can be found under the pre-process tab, and selecting filter.