Machine Learning Meetup Notes: 2010-07-07: Difference between revisions
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complete linkage: you take the largest distance instead | complete linkage: you take the largest distance instead | ||
*there is also one that takes the average | *there is also one that takes the average | ||
[Helpful WEKA Videos http://sentimentmining.net/weka/] |
Revision as of 22:20, 21 July 2010
- Col-1: patient ID
- Col-2: responder status ("1" for patients who improved and "0" otherwise)
- Col-3: Protease nucleotide sequence (if available)
- Col-4: Reverse Transciptase nucleotide sequence (if available)
- Col-5: viral load at the beginning of therapy (log-10 units)
- Col-6: CD4 count at the beginning of therapy
molecular weight and length of "PR Sequence" and "RT Sequence" from the training data
- start weka
- open mweight.csv
- remove patient
- select resp
- filter->unsupervised->attribute->numerictonominal
- click to change to first only
- apply
neural network classify->functions->multilayerperceptron
- resp
- start
- 738 correct predictions a=0 no improvement
- 66 correct predictions b=1 improvement
- 56 no improvement classified as improvement
- 140 improvement classified as no improvement
how well did it do? 80.4% accuracy
- rows tell you what really happenned
- columns tell you what was predicted
cluster simplekmeans
- change num clusters 5
- ok->start
scipy cluster.hierarchy main function called linkage ldist takes levenstein distance of each parts of the set result is a matrix distance hierarchical clustering
single linkage clustering: start with n clusters, take the ones that have the shortest distance between them and make that a cluster. then keep going until you have 1 cluster.
- when you join two points, you always check both of the distances in that cluster against other points, and then take whatever is smaller
complete linkage: you take the largest distance instead
- there is also one that takes the average
[Helpful WEKA Videos http://sentimentmining.net/weka/]