Machine Learning Meetup Notes: 2010-07-07 - Revision history

SpammerHellDontDelete at 05:21, 22 July 2010

2010-07-22T05:21:24Z

← Older revision		Revision as of 22:21, 21 July 2010
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	*Col-5: viral load at the beginning of therapy (log-10 units)		*Col-5: viral load at the beginning of therapy (log-10 units)
	*Col-6: CD4 count at the beginning of therapy		*Col-6: CD4 count at the beginning of therapy

			Helpful WEKA Videos http://sentimentmining.net/weka/

	molecular weight and length of "PR Sequence" and "RT Sequence" from the training data		molecular weight and length of "PR Sequence" and "RT Sequence" from the training data
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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/]~~

SpammerHellDontDelete at 05:20, 22 July 2010

2010-07-22T05:20:42Z

← Older revision		Revision as of 22:20, 21 July 2010
Line 43:		Line 43:
	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/]

SpammerHellDontDelete at 05:18, 22 July 2010

2010-07-22T05:18:28Z

← Older revision		Revision as of 22:18, 21 July 2010
Line 1:		Line 1:
	Col-1: patient ID		*Col-1: patient ID
	Col-2: responder status ("1" for patients who improved and "0" otherwise)		*Col-2: responder status ("1" for patients who improved and "0" otherwise)
	Col-3: Protease nucleotide sequence (if available)		*Col-3: Protease nucleotide sequence (if available)
	Col-4: Reverse Transciptase 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-5: viral load at the beginning of therapy (log-10 units)
	Col-6: CD4 count at the beginning of therapy		*Col-6: CD4 count at the beginning of therapy

	molecular weight and length of "PR Sequence" and "RT Sequence" from the training data		molecular weight and length of "PR Sequence" and "RT Sequence" from the training data
	start weka		#start weka
	open mweight.csv		#open mweight.csv
	remove patient		#remove patient
	select resp		#select resp
	filter->unsupervised->attribute->numerictonominal		#filter->unsupervised->attribute->numerictonominal
	click to change to first only		#click to change to first only
	apply		#apply

	neural network		neural network
	classify->functions->multilayerperceptron		classify->functions->multilayerperceptron
	resp		#resp
	start		#start
	738 correct predictions a=0 no improvement		*738 correct predictions a=0 no improvement
	66 correct predictions b=1 improvement		*66 correct predictions b=1 improvement

	56 no improvement classified as improvement		*56 no improvement classified as improvement
	140 improvement classified as no improvement		*140 improvement classified as no improvement

	how well did it do? 80.4% accuracy		how well did it do? 80.4% accuracy
			*rows tell you what really happenned
	rows tell you what really happenned		*columns tell you what was predicted
	columns tell you what was predicted

	cluster simplekmeans		cluster simplekmeans
	change num clusters 5		#change num clusters 5
	ok->start		#ok->start

	scipy cluster.hierarchy		scipy cluster.hierarchy
Line 41:		Line 40:

	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.		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		*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		complete linkage: you take the largest distance instead
	-there is also one that takes the average		*there is also one that takes the average

SpammerHellDontDelete: Created page with '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 nucleot…'

2010-07-22T05:16:25Z

Created page with '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 nucleot…'

New page

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