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# 第8回「集合知プログラミング」真面目に勉強する会 @外苑前

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### 第8回「集合知プログラミング」真面目に勉強する会 @外苑前

1. 1. Chapter 8, Building Price modelsProgramming Collective Intelligenceterasaka.k2011-11-10
2. 2. Chapter 8, Building Price modelsProgramming Collective Intelligence••1.2.3.
3. 3. Chapter 8, Building Price modelsProgramming Collective Intelligence••1.2.•1.2.•••
4. 4. Chapter 8, Building Price modelsProgramming Collective Intelligence••1.2.•1.2.•••
5. 5. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →•
6. 6. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →•
7. 7. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →••1. Rating2. Age
8. 8. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →••1. Rating2. AgePeak age• (older)Peak age
9. 9. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →••1. Rating2. AgePeak age• (older)•• def wineprice(rating, age): …•• def wineset1(): …• random• rating ∈ [50-100]• age ∈ [0-50]• price *= [80-120%]Peak age = rating-50
10. 10. Chapter 8, Building Price modelsProgramming Collective Intelligence•• →••1. Rating2. AgePeak age• (older)•• def wineprice(rating, age): …•• def wineset1(): …• random• rating ∈ [50-100]• age ∈ [0-50]• price *= [80-120%]Peak age = rating-50>>> data[0]{input: (63.602840187200407, 21.574120872184949), result: 34.565257353086487}>>> data[1]{input: (74.994980945756794, 48.052051269308649), result: 0.0}
11. 11. Chapter 8, Building Price modelsProgramming Collective Intelligence
12. 12. Chapter 8, Building Price modelsProgramming Collective Intelligence••
13. 13. Chapter 8, Building Price modelsProgramming Collective Intelligence••• →as
14. 14. Chapter 8, Building Price modelsProgramming Collective Intelligence
15. 15. Chapter 8, Building Price modelsProgramming Collective Intelligence• k-Nearest Neighbors• …
16. 16. Chapter 8, Building Price modelsProgramming Collective Intelligence
17. 17. Chapter 8, Building Price modelsProgramming Collective Intelligence
18. 18. Chapter 8, Building Price modelsProgramming Collective Intelligence
19. 19. Chapter 8, Building Price modelsProgramming Collective Intelligence
20. 20. Chapter 8, Building Price modelsProgramming Collective Intelligence
21. 21. Chapter 8, Building Price modelsProgramming Collective Intelligencepeak age
22. 22. Chapter 8, Building Price modelsProgramming Collective Intelligencepeak ageSquiggle
23. 23. Chapter 8, Building Price modelsProgramming Collective IntelligenceSquiggle…peak age
24. 24. Chapter 8, Building Price modelsProgramming Collective Intelligence
25. 25. Chapter 8, Building Price modelsProgramming Collective Intelligence
26. 26. Chapter 8, Building Price modelsProgramming Collective Intelligence20
27. 27. Chapter 8, Building Price modelsProgramming Collective Intelligence20peak age
28. 28. Chapter 8, Building Price modelsProgramming Collective Intelligence20peak age
29. 29. Chapter 8, Building Price modelsProgramming Collective Intelligence20peak age
30. 30. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agek-Nearest Neighbors
31. 31. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agek
32. 32. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agek
33. 33. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agek
34. 34. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agekOptimization
35. 35. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agekOptimizationOptimization
36. 36. Chapter 8, Building Price modelsProgramming Collective Intelligencek20peak agekOptimizationOptimizationOptimization
37. 37. Programming Collective IntelligencekkOptimizationOptimizationOptimizationChapter 8, Building Price models20peak age
38. 38. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n
39. 39. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269
40. 40. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelist
41. 41. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelistdef knnestimate(data,vec1,k=5): …getdistances k𝑒𝑢𝑐𝑙𝑖𝑑𝑖𝑎𝑛(𝑣𝑒𝑐1, 𝑣𝑖)𝑖≤𝑘 /k
42. 42. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelistdef knnestimate(data,vec1,k=5): …getdistances k𝑒𝑢𝑐𝑙𝑖𝑑𝑖𝑎𝑛(𝑣𝑒𝑐1, 𝑣𝑖)𝑖≤𝑘 /kk
43. 43. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelistdef knnestimate(data,vec1,k=5): …getdistances k𝑒𝑢𝑐𝑙𝑖𝑑𝑖𝑎𝑛(𝑣𝑒𝑐1, 𝑣𝑖)𝑖≤𝑘 /kk>>> numpredict.knnestimate(data,(95.0,3.0))29.176138546872018>>> numpredict.knnestimate(data,(99.0,3.0))22.356856188108672
44. 44. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelistdef knnestimate(data,vec1,k=5): …getdistances k𝑒𝑢𝑐𝑙𝑖𝑑𝑖𝑎𝑛(𝑣𝑒𝑐1, 𝑣𝑖)𝑖≤𝑘 /kk>>> numpredict.knnestimate(data,(95.0,3.0))29.176138546872018>>> numpredict.knnestimate(data,(99.0,3.0))22.356856188108672
45. 45. Chapter 8, Building Price modelsProgramming Collective Intelligence• def euclidean(v1,v2): …n>>> numpredict.euclidean(data[0][input],data[1][input])28.56386131112269def getdistances(data,vec1):distancelist=[]for i in range(len(data)):vec2=data[i][input]distancelist.append((euclidean(vec1,vec2),i))distancelist.sort( )return distancelistdef knnestimate(data,vec1,k=5): …getdistances k𝑒𝑢𝑐𝑙𝑖𝑑𝑖𝑎𝑛(𝑣𝑒𝑐1, 𝑣𝑖)𝑖≤𝑘 /kk>>> numpredict.knnestimate(data,(95.0,3.0))29.176138546872018>>> numpredict.knnestimate(data,(99.0,3.0))22.356856188108672kNN weakness
46. 46. Chapter 8, Building Price modelsProgramming Collective Intelligence
47. 47. Chapter 8, Building Price modelsProgramming Collective Intelligence•
48. 48. Chapter 8, Building Price modelsProgramming Collective Intelligence••
49. 49. Chapter 8, Building Price modelsProgramming Collective Intelligence•
50. 50. Chapter 8, Building Price modelsProgramming Collective Intelligence•def inverseweight(dist,num=1.0,const=0.1):return num/(dist+const)
51. 51. Chapter 8, Building Price modelsProgramming Collective Intelligence•def inverseweight(dist,num=1.0,const=0.1):return num/(dist+const)→
52. 52. Chapter 8, Building Price modelsProgramming Collective Intelligence•
53. 53. Chapter 8, Building Price modelsProgramming Collective Intelligence•→ >0
54. 54. Chapter 8, Building Price modelsProgramming Collective Intelligence
55. 55. Chapter 8, Building Price modelsProgramming Collective Intelligence
56. 56. Chapter 8, Building Price modelsProgramming Collective Intelligence𝑓 𝑥 = 𝑒−dist22𝜎2
57. 57. Chapter 8, Building Price modelsProgramming Collective Intelligence
58. 58. Chapter 8, Building Price modelsProgramming Collective Intelligenceweight distance 𝑣𝑒𝑐1, 𝑖 th nearest 𝑣𝑒𝑐 ∗ value 𝑖total weigh𝑡𝑖<𝑘
59. 59. Chapter 8, Building Price modelsProgramming Collective Intelligenceweight distance 𝑣𝑒𝑐1, 𝑖 th nearest 𝑣𝑒𝑐 ∗ value 𝑖total weigh𝑡𝑖<𝑘>>> numpredict.weightedknn(data,(99.0,5.0))32.640981119354301
60. 60. Chapter 8, Building Price modelsProgramming Collective Intelligenceweight distance 𝑣𝑒𝑐1, 𝑖 th nearest 𝑣𝑒𝑐 ∗ value 𝑖total weigh𝑡𝑖<𝑘>>> numpredict.weightedknn(data,(99.0,5.0))32.640981119354301rigorous
61. 61. Chapter 8, Building Price modelsProgramming Collective Intelligenceweight distance 𝑣𝑒𝑐1, 𝑖 th nearest 𝑣𝑒𝑐 ∗ value 𝑖total weigh𝑡𝑖<𝑘>>> numpredict.weightedknn(data,(99.0,5.0))32.640981119354301rigorous
62. 62. Chapter 8, Building Price modelsProgramming Collective Intelligence
63. 63. Chapter 8, Building Price modelsProgramming Collective Intelligence
64. 64. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique
65. 65. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set
66. 66. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set95%
67. 67. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•
68. 68. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set
69. 69. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set
70. 70. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
71. 71. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
72. 72. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
73. 73. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
74. 74. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
75. 75. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
76. 76. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.
77. 77. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def dividedata(data,test=0.05):…return trainset,testset
78. 78. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def testalgorithm(algf,trainset,testset):…return
79. 79. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def testalgorithm(algf,trainset,testset):…return− 2𝑡𝑒𝑠𝑡𝑠𝑒𝑡𝑡𝑒𝑠𝑡𝑠𝑒𝑡
80. 80. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def testalgorithm(algf,trainset,testset):…return− 2𝑡𝑒𝑠𝑡𝑠𝑒𝑡𝑡𝑒𝑠𝑡𝑠𝑒𝑡def crossvalidate(algf,data,trials=100,test=0.05):…return testalgorithm trials
81. 81. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def testalgorithm(algf,trainset,testset):…return− 2𝑡𝑒𝑠𝑡𝑠𝑒𝑡𝑡𝑒𝑠𝑡𝑠𝑒𝑡def crossvalidate(algf,data,trials=100,test=0.05):…return testalgorithm trialsalgf gaussian
82. 82. Chapter 8, Building Price modelsProgramming Collective Intelligence• technique• Training set Test set•1.Training set2.Test set3.def testalgorithm(algf,trainset,testset):…return− 2𝑡𝑒𝑠𝑡𝑠𝑒𝑡𝑡𝑒𝑠𝑡𝑠𝑒𝑡def crossvalidate(algf,data,trials=100,test=0.05):…return testalgorithm trialsalgf gaussian
83. 83. Chapter 8, Building Price modelsProgramming Collective Intelligence
84. 84. Chapter 8, Building Price modelsProgramming Collective Intelligence
85. 85. Chapter 8, Building Price modelsProgramming Collective Intelligence••
86. 86. Chapter 8, Building Price modelsProgramming Collective Intelligence••• →
87. 87. Chapter 8, Building Price modelsProgramming Collective Intelligence••• →rating, age, aisle, bottlesize• aisle• bottlesize (ml)
88. 88. Chapter 8, Building Price modelsProgramming Collective Intelligence••• →rating, age, aisle, bottlesize• aisle• bottlesize (ml)
89. 89. Chapter 8, Building Price modelsProgramming Collective Intelligence
90. 90. Chapter 8, Building Price modelsProgramming Collective Intelligence• i i
91. 91. Chapter 8, Building Price modelsProgramming Collective Intelligence• i i•
92. 92. Chapter 8, Building Price modelsProgramming Collective Intelligence• i i•def rescale(data,scale):… i scale[i]return scaleddata
93. 93. Chapter 8, Building Price modelsProgramming Collective Intelligence• i i•def rescale(data,scale):… i scale[i]return scaleddata
94. 94. Chapter 8, Building Price modelsProgramming Collective Intelligence
95. 95. Chapter 8, Building Price modelsProgramming Collective Intelligence•
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100. 100. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization
101. 101. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•
102. 102. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization••
103. 103. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••
104. 104. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costf
105. 105. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfscale algf
106. 106. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costf
107. 107. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4
108. 108. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4
109. 109. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4
110. 110. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4>>>import optimization>>>costf=numpredict.createcostfunction(numpredict.knnestimate,data)>>> optimization.annealingoptimize(numpredict.weightdomain,costf,step=2)[11,18,0,6]
111. 111. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4>>>import optimization>>>costf=numpredict.createcostfunction(numpredict.knnestimate,data)>>> optimization.annealingoptimize(numpredict.weightdomain,costf,step=2)[11,18,0,6]rating, age, aisle, bottlesizeaisle
112. 112. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4>>>import optimization>>>costf=numpredict.createcostfunction(numpredict.knnestimate,data)>>> optimization.annealingoptimize(numpredict.weightdomain,costf,step=2)[11,18,0,6]bottlesize
113. 113. Chapter 8, Building Price modelsProgramming Collective Intelligence• Chapter 5 Optimization•••def createcostfunction(algf,data):def costf(scale):sdata=rescale(data,scale)return crossvalidate(algf,sdata,trials=10)return costfweightdomain=[(0,20)]*4
114. 114. Chapter 8, Building Price modelsProgramming Collective Intelligence
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122. 122. Chapter 8, Building Price modelsProgramming Collective Intelligence•1/2 60%def wineset3():…
123. 123. Chapter 8, Building Price modelsProgramming Collective Intelligence•1/2 60%def wineset3():…
124. 124. Chapter 8, Building Price modelsProgramming Collective Intelligence••
125. 125. Chapter 8, Building Price modelsProgramming Collective Intelligence••• 60% -• 35% -• …
126. 126. Chapter 8, Building Price modelsProgramming Collective Intelligencedef probguess(data,vec1,low,high,k=5,weightf=gaussian):…return vec1 low-high
127. 127. Chapter 8, Building Price modelsProgramming Collective Intelligencedef probguess(data,vec1,low,high,k=5,weightf=gaussian):…return vec1 low-highk low-highnweight += weightreturn nweight(=low-high weight / k weight
128. 128. Chapter 8, Building Price modelsProgramming Collective Intelligence
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143. 143. Chapter 8, Building Price modelsProgramming Collective Intelligence• 0 – 5• 5 - 10• 10-15• …0Gaussian
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149. 149. Chapter 8, Building Price modelsProgramming Collective Intelligence――
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153. 153. Chapter 8, Building Price modelsProgramming Collective Intelligence――――――
154. 154. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k
155. 155. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k――
156. 156. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian
157. 157. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――
158. 158. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation
159. 159. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set
160. 160. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &
161. 161. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――
162. 162. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN
163. 163. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――
164. 164. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――
165. 165. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――
166. 166. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――
167. 167. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――
168. 168. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――
169. 169. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ――
170. 170. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ―― X-Y 60%
171. 171. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ―― X-Y 60%――
172. 172. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ―― X-Y 60%――
173. 173. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ―― X-Y 60%――
174. 174. Chapter 8, Building Price modelsProgramming Collective Intelligence―――――― k―― ――Gaussian• ――Cross-Validation• Training set test set &――kNN• ――――――• ―― X-Y 60%――
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177. 177. terasaka.kChapter 8, Building Price modelsProgramming Collective Intelligence• ――• ――• kNN―― k• ―― ――Gaussian• ――Cross-Validation• Training set test set &• ――kNN• ――• ――• ――• ―― X-Y 60%• ――
178. 178. Chapter 8, Building Price modelsProgramming Collective Intelligenceterasaka.k2011-11-09T17:39:24