Use the MBR (nearest neighbor) technique to classify this wine: (use the WINE data given in the dataset folder week1). The class attribute is the FIRST attribute. For credit you must submit the answer on the answer sheet AND the program used to calculate the answer. 12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,684
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.names
Solution
to do this we first need to identify sample, training, class sample data.
sample = [12.82; 3.37; 2.3; 19.5 ;88; 1.48; .66; 4; .97; 10.26; .72; 1.75; 684]
training = [10;20;30;40;50;60;70;80;90;100]
group = [\'Alcohol\'; \'Malic acid\'; \'Ash\'; \'Alcalinity of Ash\'; \'Magnesium\'; \'phenols\'; \'Flavanoids\'; \'nonfalvanoid phenls\'; \'hue\'; \'proline\']
class = knnclassify(sample, training, group)
---------------------------------------------- You can use fitcknn instead of knnclassify also ------------------------------
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Use the MBR (nearest neighbor) technique to classify this wine- (use t.docx
1. Use the MBR (nearest neighbor) technique to classify this wine: (use the WINE data given in the
dataset folder week1). The class attribute is the FIRST attribute. For credit you must submit the
answer on the answer sheet AND the program used to calculate the answer.
12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,684
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.names
Solution
to do this we first need to identify sample, training, class sample data.
sample = [12.82; 3.37; 2.3; 19.5 ;88; 1.48; .66; 4; .97; 10.26; .72; 1.75; 684]
training = [10;20;30;40;50;60;70;80;90;100]
group = ['Alcohol'; 'Malic acid'; 'Ash'; 'Alcalinity of Ash'; 'Magnesium'; 'phenols';
'Flavanoids'; 'nonfalvanoid phenls'; 'hue'; 'proline']
class = knnclassify(sample, training, group)
---------------------------------------------- You can use fitcknn instead of knnclassify also ----------
--------------------
![Use the MBR (nearest neighbor) technique to classify this wine: (use the WINE data given in the
dataset folder week1). The class attribute is the FIRST attribute. For credit you must submit the
answer on the answer sheet AND the program used to calculate the answer.
12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,684
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.names
Solution
to do this we first need to identify sample, training, class sample data.
sample = [12.82; 3.37; 2.3; 19.5 ;88; 1.48; .66; 4; .97; 10.26; .72; 1.75; 684]
training = [10;20;30;40;50;60;70;80;90;100]
group = ['Alcohol'; 'Malic acid'; 'Ash'; 'Alcalinity of Ash'; 'Magnesium'; 'phenols';
'Flavanoids'; 'nonfalvanoid phenls'; 'hue'; 'proline']
class = knnclassify(sample, training, group)
---------------------------------------------- You can use fitcknn instead of knnclassify also ----------
--------------------](https://image.slidesharecdn.com/usethembrnearestneighbortechniquetoclassifythiswine-uset-230204014207-eb14a0d9/85/Use-the-MBR-nearest-neighbor-technique-to-classify-this-wine-use-t-docx-1-320.jpg)
