Probability Overview Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
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1 Probability Overview Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
2 머신러닝의학습방법들 - Gradient descent based learning - Probability theory based learning - Information theory based learning - Distance similarity based learning
3 머신러닝의학습방법들 - Gradient descent based learning - Probability theory based learning - Information theory based learning - Distance similarity based learning
4 Probability - 이산형값 count(event X) P (X) = count(all Event) - 연속형값 P ( 1 <x<1) Z 1 1 f(x) dx =1 Source: Source:
5 Basic concepts of probability 0 apple P (E) apple 1 P (S) = NX i=1 P (E i ) = 1 if all E i are independent P (A \ B) P (A [ B) P (A c )=1 P (A)
6 Conditional probability A B P (A B) = P (A \ B) P (B) A \ B
7 Conditional probability P (A B) = P (A \ B) P (B) P (A) = {set of odd number} P (B) = {less than 4 in a dice} P (B A) = P (A \ B) P (A) A B A \ B
8 Human knowledge belongs to the world.
9 Bayes s Theorem Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
10 Bayes s theorem - 경험에의한확률의업데이트 - 사전확률 (given) 에서사건발생을통해사후확률로 - 빈도주의 vs. 베이즈주의 - 객관적확률은존재하지않는다!
11 Queen card game
12
13 Probability updated
14 Probability updated
15 Bayes s theorem P (A B) = P (A \ B) P (B) P (B A) = P (A \ B) P (A) P (A \ B) =P (B)P (A B) =P (A)P (B A) P (A B) = P (A \ B) P (B) = P (A)P (B A) P (B)
16 Cookie Quiz 쿠키두그릇이있다고한다. 첫번째그릇에는바닐라쿠키 30개와초콜렛쿠키 10개가있고, 두번째그릇에는각쿠키가 20개씩있다. 임의쿠키를집었는데해당쿠키가바닐라쿠키이다. 이쿠키가그릇 1에서나왔을확률은? Source:
17 Cookie Quiz 쿠키두그릇이있다고한다. 첫번째그릇에는바닐라쿠키 30 개와초콜렛쿠키 10 개가있고, 두번째그릇에는각 쿠키가 20 개씩있다. 임의쿠키를집었는데해당쿠키가바닐라쿠키이다. 이쿠키가그릇 1 에서나왔을확률은? P (Choco) P (A B) = P (A \ B) P (B) = P (A)P (B A) P (B) P (Vanilla) P (B1) P (B2) Source:
18 <latexit sha1_base64="sud+tka9qbtmtepj5vavo7nckmu=">aaacchicbzbps8mwgmbt+w/of1wpxojdwc+jfue9cen32lgcdyotjdrlt7a0lukqjg5xl34vlx5uvporvpltzlyedpobwc/p+74k7xmkjepl299gywv1bx2jufna2t7z3tp3d+5lnapmpbyzwlqcjamjnhikkkzaisaochhpbsobab35qiskmb9to4t4eepzglkmlla6jnqrjxhdglewewqem3213ep93lammuvwpguw7ao9e1wgj4cyyov2za9ol8zprljcdenzduxe+rksimjgjqvokkmc8bd1svsjrxgrfjbbzajptnodysz04qro3n8tgyqkhewb7oyqgsjf2tt8r9zovxjhz5qnqsiczx8kuwzvdkexwb4vbcs20ocwopqvea+qtktp8eo6bgdx5wxwtquxvef2rfy7ztmoginwdcraaeegbhrabr7a4be8g1fwzjwzl8a78tfvlrj5zch4i+pzb4jfl0q=</latexit> <latexit sha1_base64="sud+tka9qbtmtepj5vavo7nckmu=">aaacchicbzbps8mwgmbt+w/of1wpxojdwc+jfue9cen32lgcdyotjdrlt7a0lukqjg5xl34vlx5uvporvpltzlyedpobwc/p+74k7xmkjepl299gywv1bx2jufna2t7z3tp3d+5lnapmpbyzwlqcjamjnhikkkzaisaochhpbsobab35qiskmb9to4t4eepzglkmlla6jnqrjxhdglewewqem3213ep93lammuvwpguw7ao9e1wgj4cyyov2za9ol8zprljcdenzduxe+rksimjgjqvokkmc8bd1svsjrxgrfjbbzajptnodysz04qro3n8tgyqkhewb7oyqgsjf2tt8r9zovxjhz5qnqsiczx8kuwzvdkexwb4vbcs20ocwopqvea+qtktp8eo6bgdx5wxwtquxvef2rfy7ztmoginwdcraaeegbhrabr7a4be8g1fwzjwzl8a78tfvlrj5zch4i+pzb4jfl0q=</latexit> <latexit sha1_base64="sud+tka9qbtmtepj5vavo7nckmu=">aaacchicbzbps8mwgmbt+w/of1wpxojdwc+jfue9cen32lgcdyotjdrlt7a0lukqjg5xl34vlx5uvporvpltzlyedpobwc/p+74k7xmkjepl299gywv1bx2jufna2t7z3tp3d+5lnapmpbyzwlqcjamjnhikkkzaisaochhpbsobab35qiskmb9to4t4eepzglkmlla6jnqrjxhdglewewqem3213ep93lammuvwpguw7ao9e1wgj4cyyov2za9ol8zprljcdenzduxe+rksimjgjqvokkmc8bd1svsjrxgrfjbbzajptnodysz04qro3n8tgyqkhewb7oyqgsjf2tt8r9zovxjhz5qnqsiczx8kuwzvdkexwb4vbcs20ocwopqvea+qtktp8eo6bgdx5wxwtquxvef2rfy7ztmoginwdcraaeegbhrabr7a4be8g1fwzjwzl8a78tfvlrj5zch4i+pzb4jfl0q=</latexit> <latexit sha1_base64="sud+tka9qbtmtepj5vavo7nckmu=">aaacchicbzbps8mwgmbt+w/of1wpxojdwc+jfue9cen32lgcdyotjdrlt7a0lukqjg5xl34vlx5uvporvpltzlyedpobwc/p+74k7xmkjepl299gywv1bx2jufna2t7z3tp3d+5lnapmpbyzwlqcjamjnhikkkzaisaochhpbsobab35qiskmb9to4t4eepzglkmlla6jnqrjxhdglewewqem3213ep93lammuvwpguw7ao9e1wgj4cyyov2za9ol8zprljcdenzduxe+rksimjgjqvokkmc8bd1svsjrxgrfjbbzajptnodysz04qro3n8tgyqkhewb7oyqgsjf2tt8r9zovxjhz5qnqsiczx8kuwzvdkexwb4vbcs20ocwopqvea+qtktp8eo6bgdx5wxwtquxvef2rfy7ztmoginwdcraaeegbhrabr7a4be8g1fwzjwzl8a78tfvlrj5zch4i+pzb4jfl0q=</latexit> Bayes s theorem H is Class 사전확률우도사후확률 P (H)P (D H) P (H D) = P (D) D is Data 데이터가발생할확률 (Evidence)
19 Bayes s theorem P (H D) = P (C)P (D H) P (D) P (A 1 \ B)+P (A 2 \ B)+P (A 3 \ B) P (B) =P (A 1 )P (B \ A 1 )+P (A 2 )P (B \ A 2 )+P (A 3 )P (B \ A 3 ) P (A \ B) =P (B)P (A B) =P (A)P (B A)
20 Example 공 1 무작위로 1 개나오는다음과같은완구가있다. 나온공이 큰공 일때, 검은색일확률은? Source:
21 Example P (H D) = P (C)P (D H) P (D) P (B) =P (A 1 )P (B \ A 1 )+P (A 2 )P (B \ A 2 )+P (A 3 )P (B \ A 3 ) Source:
22 Human knowledge belongs to the world.
23 Simple Bayes Classifier Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
24 Viagra 스팸필터기 - Viagra 라는단어의유무를통해스팸여부확인 - Viagra 단어가들어가면무조건스팸? - 어느정도확률로스팸이라고해야할까?
25 Viagra 스팸필터기 number viagra spam number viagra spam
26 Viagra 스팸필터기
27 Viagra 스팸필터기
28
29 Human knowledge belongs to the world.
30 NB Classifier Overview Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
31 제대로된스팸필터기를만들어보자 - Viagra 단어외에영향을주는단어들은? - 오히려스팸을제외해주는단어는어떻게찾지? - 한번에여러단어들을고려하는필터기를만들자
32 Feature 의확장 P (spam viagra) P (spam viagra, hello, lucky, marketing...) 변수가많을때, 조건부확률의변화
33 Multivariate multiplication rule P (Y X 1,X 2 )= P (Y \ X 1 \ X 2 ) P (X 1 \ X 2 ) P (Y \ X 1 \ X 2 )=P (Y X 1,X 2 )P (X 1 \ X 2 ) P (X 1,X 2,X 3,...X n ) = P (X 1 )P (X 2 X 1 )P (X 3 X 1,X 2 )...P(X n X 1...X x 1 )
34 Problems - 계산이어려워짐 - Feature 의차원이증가하면 Sparse Vector 가생성 à 확률이 0 이되는값이늘어남
35 Naïve Bayes Classifier - 복잡하게하지말고단순 (naïve) 하게해결하자 - 각변수의관계가독립임을가정 - 계산이용이해지고, 성능이생각보다좋음
36 Joint Probability P (A \ B) =P (A)P (B) if A and B are independent P (Y X 1 \ X 2 )= P (Y )P (X 1 \ X 2 Y ) P (X 1 \ X 2 ) P (Y )P (X 1 Y )P (X 2 Y ) P (X 1 )P (X 2 )
37 Naïve Bayes Classifier P (Y X 1 \ X 2 )= P (Y )P (X 1 \ X 2 Y ) P (X 1 \ X 2 ) = P (Y )P (X 1 Y )P (X 2 Y ) P (X 1 )P (X 2 ) P (Y c X 1,...,X n )= P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) Y c is a label
38 Issues - 너무많은확률값 à 0 에수렴하게되는문제 - 곱하지말고더하자 à log ny log{p (Y c ) P (X i Y c )} = log P (Y c )+ i=1 nx i=1 log P (X i Y c ) P (Y c X 1,...,X n )= P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) Y c is a label
39 Issues - 확률이 0 인변수들이존재함 à 전체값 0 - 작게나마확률이나올수있도록변경 à 스무딩 P (X Y )= count(x \ Y )+k count(y )+(k number of class ) log{p (Y c ) ny i=1 P (X i Y c )} = log P (Y c )+ nx log P (X i Y c ) i=1
40 Human knowledge belongs to the world.
41 NB Classifier Implementation Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
42 Dataset German Credit - 대출사기인가? 아닌가를예측하는문제 - 데이터를 NB 에맞도록간단하게변환 - Binary 데이터들로이루어진대출사기데이터
43
44 Preprocessing One-Hot Encoding
45
46 <latexit sha1_base64="4550uoa6suvf95ouyo3s5kzkirc=">aaacdhicfvfnb9naef2br2k+alyqenlqufiivzhdc3covmgfy5aidyqdnv6v21xxa7m7rkrb/wn+htf+bheubnicoeu8aawnn280s2+kvkllsfijck9dv3hz1s7t6m7de/cfdb4++mcbznax441qzlxak5tuykaslji3rmbdkhfcnl1z14+/cgnlo9/tqhxlgk+0rcrh8li++bbh0xt0medwdvm83yesbmjue67hcahzzzc7jwgctaypmyvrstz38jdtpzndw3q0z+x52tc7/1ngpwsfoywhi/gvxow7ypawebqwqvvrhoedytjjnocrjn2sidtimg+++3v5vwtnxkg1izrpaenqkork9fhwwdeip8mtsfbuyy3s0m1s62hpkyvujffpe2zupzsc1tau6si7a6rte7m2fv9vw3ruvvw6qduohoyxg6poatwwpggu0ghoauujcip9rsbp0qdn/lcrdyg9/owrzhywetvj3x0mj15v09hht9kug7guvwbh7c2bshnj7gfwjhge7aa/wmfhmny7sibbtucx+wvh5dcdllqp</latexit> <latexit sha1_base64="4550uoa6suvf95ouyo3s5kzkirc=">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</latexit> <latexit sha1_base64="4550uoa6suvf95ouyo3s5kzkirc=">aaacdhicfvfnb9naef2br2k+alyqenlqufiivzhdc3covmgfy5aidyqdnv6v21xxa7m7rkrb/wn+htf+bheubnicoeu8aawnn280s2+kvkllsfijck9dv3hz1s7t6m7de/cfdb4++mcbznax441qzlxak5tuykaslji3rmbdkhfcnl1z14+/cgnlo9/tqhxlgk+0rcrh8li++bbh0xt0medwdvm83yesbmjue67hcahzzzc7jwgctaypmyvrstz38jdtpzndw3q0z+x52tc7/1ngpwsfoywhi/gvxow7ypawebqwqvvrhoedytjjnocrjn2sidtimg+++3v5vwtnxkg1izrpaenqkork9fhwwdeip8mtsfbuyy3s0m1s62hpkyvujffpe2zupzsc1tau6si7a6rte7m2fv9vw3ruvvw6qduohoyxg6poatwwpggu0ghoauujcip9rsbp0qdn/lcrdyg9/owrzhywetvj3x0mj15v09hht9kug7guvwbh7c2bshnj7gfwjhge7aa/wmfhmny7sibbtucx+wvh5dcdllqp</latexit> <latexit sha1_base64="4550uoa6suvf95ouyo3s5kzkirc=">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</latexit> P (Y c X 1,...,X n )= Modelling P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) Y c is a label
47 Modelling Y 의 Index P(X $ Y ' )
48 Classifier P(Y=1 X) 의확률과 P(Y=0 X) 의확률비교
49 Human knowledge belongs to the world.
50 Multinomial NB Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
51 Multinomial Naïve Bayes - X 값이 Binary 가아니라 1 이상의값을가지는문제 - 일반적으로 Text 문제를분류할때많이쓰임 - 단어의존재유무가아닌단어의출현횟수 Feature 로
52 Text 의 Feature 표현? 문자 è Feature
53 문자를 Vector 로 One-hot Encoding 하나의단어를 Vector 의 Index 로인식, 단어존재시 1 없으면 0
54 Bag of words 단어별로인덱스를부여해서 한문장 ( 또는문서 ) 의단어의개수를 Vector 로표현
55 Bag of words 단어별로인덱스를부여해서 한문장 ( 또는문서 ) 의단어의개수를 Vector 로표현
56 다시돌아가서..
57 Multinomial Naïve Bayes P (Y c X 1,...,X n )= P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) 기본식!! Y c is a label
58 Multinomial Naïve Bayes P (Y c X 1,...,X n )= P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) Y c is a label Likelihood 만바뀜
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sha1_base64="vhpckempjfbbycnxptzebx4flfk=">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</latexit> <latexit sha1_base64="vhpckempjfbbycnxptzebx4flfk=">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</latexit> Multinomial Naïve Bayes - 식계산하는방식이다름 ny P (X i Y c ) P (X i Y c )= i=1 P tf(xi,d2 Y c )+ P Nd2Yc + V x_i: A word from the feature vector x of a particular sample. tf(xi,d y_c): The sum of raw term frequencies of word x_i from all documents in the training sample that belong to class y_c. Nd y_c: The sum of all term frequencies in the training dataset for class y_c. α: An additive smoothing parameter (α=1α=1 for Laplace smoothing). V: The size of the vocabulary (number of different words in the training set).
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61 Multinomial Naïve Bayes P (Y c X 1,...,X n )= P (Y c ) n Q i=1 P (X i Y c ) nq P (X i ) i=1
62 Human knowledge belongs to the world.
63 Gaussian NB Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
64 Gaussian Naïve Bayes - Category 데이터가아닌경우에 NB 의적용 - Continuous 데이터의적용을위해 y의분포를정규분포 (gaussian) 으로가정함 - 확률밀도함수상의해당값 x 가나올확률로 NB를구현함
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sha1_base64="spnegus/j04plu+gx72elzjdcy4=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug9flx4rgltpq9lsn+3szsbstoqs+ho8efdx6j/y5r9x2+ag1qcdj/dmmjkxpliydn0vp7s0vlk6vl6vbgxube9ud/futzjpxn2wyes3q2q4fir7kfdydqo5jupjw+hoauq3hrk2ilf3oe55enobepfgfk10+9bjvwrnrbszkl/ek0gncjr71c9up2fzzbuysy3peg6kqu41cib5pnlnde8pg9eb71iqamxnkm9onzajq/rjlghbcslm/tmr09iycrzazpji0cx6u/e/r5nhdb7kqquzcsxmi6jmekzi9g/sf5ozlgnlknpc3krykgrk0kztssf4iy//jf5j/alu3zzwgpdfgmu4gem4bg/ooahx0aqfgazgcv7g1zhos/pmvm9bs04xsw+/4hx8a5xwjyc=</latexit> <latexit 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sha1_base64="/ixp9j8uray/ibhcslp/e60nneo=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug9flx4rgltoq9lsj+3szsbsboqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t941emmgposeylqh1sj4bj9w43adqqqxqhavji6mfqtj1saj/lbjfmmyjqqpokmgivdt3u8v625dxcgsky8gtsgqlnx/er2e5bfka0tvouo56ymykkynamcvlqzxpsyer1gx1jjy9rbpjt1qk6s0idromxjq2bq74mcxlqp49b2xtqm9ai3ff/zopmjloocyzqzknl8uzqjyhiy/zv0uujmxngsyhs3txi2pioyy9op2bc8xzexix9wv6p7d+e1xnwrrhmo4bhowymlamatnmehbgn4hld4c4tz4rw7h/pwklpmhmifoj8/nx2nja==</latexit> <latexit sha1_base64="/ixp9j8uray/ibhcslp/e60nneo=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug9flx4rgltoq9lsj+3szsbsboqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t941emmgposeylqh1sj4bj9w43adqqqxqhavji6mfqtj1saj/lbjfmmyjqqpokmgivdt3u8v625dxcgsky8gtsgqlnx/er2e5bfka0tvouo56ymykkynamcvlqzxpsyer1gx1jjy9rbpjt1qk6s0idromxjq2bq74mcxlqp49b2xtqm9ai3ff/zopmjloocyzqzknl8uzqjyhiy/zv0uujmxngsyhs3txi2pioyy9op2bc8xzexix9wv6p7d+e1xnwrrhmo4bhowymlamatnmehbgn4hld4c4tz4rw7h/pwklpmhmifoj8/nx2nja==</latexit> <latexit sha1_base64="/ixp9j8uray/ibhcslp/e60nneo=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug9flx4rgltoq9lsj+3szsbsboqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t941emmgposeylqh1sj4bj9w43adqqqxqhavji6mfqtj1saj/lbjfmmyjqqpokmgivdt3u8v625dxcgsky8gtsgqlnx/er2e5bfka0tvouo56ymykkynamcvlqzxpsyer1gx1jjy9rbpjt1qk6s0idromxjq2bq74mcxlqp49b2xtqm9ai3ff/zopmjloocyzqzknl8uzqjyhiy/zv0uujmxngsyhs3txi2pioyy9op2bc8xzexix9wv6p7d+e1xnwrrhmo4bhowymlamatnmehbgn4hld4c4tz4rw7h/pwklpmhmifoj8/nx2nja==</latexit> <latexit sha1_base64="/ixp9j8uray/ibhcslp/e60nneo=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug9flx4rgltoq9lsj+3szsbsboqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t941emmgposeylqh1sj4bj9w43adqqqxqhavji6mfqtj1saj/lbjfmmyjqqpokmgivdt3u8v625dxcgsky8gtsgqlnx/er2e5bfka0tvouo56ymykkynamcvlqzxpsyer1gx1jjy9rbpjt1qk6s0idromxjq2bq74mcxlqp49b2xtqm9ai3ff/zopmjloocyzqzknl8uzqjyhiy/zv0uujmxngsyhs3txi2pioyy9op2bc8xzexix9wv6p7d+e1xnwrrhmo4bhowymlamatnmehbgn4hld4c4tz4rw7h/pwklpmhmifoj8/nx2nja==</latexit> <latexit sha1_base64="lwamupo1efsfsmwyisfcbrsynwi=">aaab8xicbvbns8naen3ur1q/qh69lbbbg5rebpvw9okxgrgfjjtndtmu3c2g3ylqqn+gfw8qxv033vw3btsctpxbwoo9gwbmxzngblz326msrk6tb1q3a1vbo7t79f2dr6nytzlplvc6gxpdbe+zdxwe62aaerkl1olht1o/88s04sp9ghhgikkgku84jwclijt5gq4nh0jsqzfcpjsdxizesrqorltx/wr7iuaspuafmsbw3ayigmjgvlbjlcwnywgdkqelle2jzcyqzidp8ilv+jhr2lykekb+niiingysy9spcqznojcv//ochjkrqobplgnl6xxrkgsmck//x32ugquxtorqze2tma6jjhrssjubgrf48jlxz5vxte/+otg6kdoooin0je6rhy5rc92hnvirrqo9o1f05odz4rw7h/pwilpohki/cd5/acjuklm=</latexit> <latexit sha1_base64="lwamupo1efsfsmwyisfcbrsynwi=">aaab8xicbvbns8naen3ur1q/qh69lbbbg5rebpvw9okxgrgfjjtndtmu3c2g3ylqqn+gfw8qxv033vw3btsctpxbwoo9gwbmxzngblz326msrk6tb1q3a1vbo7t79f2dr6nytzlplvc6gxpdbe+zdxwe62aaerkl1olht1o/88s04sp9ghhgikkgku84jwclijt5gq4nh0jsqzfcpjsdxizesrqorltx/wr7iuaspuafmsbw3ayigmjgvlbjlcwnywgdkqelle2jzcyqzidp8ilv+jhr2lykekb+niiingysy9spcqznojcv//ochjkrqobplgnl6xxrkgsmck//x32ugquxtorqze2tma6jjhrssjubgrf48jlxz5vxte/+otg6kdoooin0je6rhy5rc92hnvirrqo9o1f05odz4rw7h/pwilpohki/cd5/acjuklm=</latexit> <latexit sha1_base64="lwamupo1efsfsmwyisfcbrsynwi=">aaab8xicbvbns8naen3ur1q/qh69lbbbg5rebpvw9okxgrgfjjtndtmu3c2g3ylqqn+gfw8qxv033vw3btsctpxbwoo9gwbmxzngblz326msrk6tb1q3a1vbo7t79f2dr6nytzlplvc6gxpdbe+zdxwe62aaerkl1olht1o/88s04sp9ghhgikkgku84jwclijt5gq4nh0jsqzfcpjsdxizesrqorltx/wr7iuaspuafmsbw3ayigmjgvlbjlcwnywgdkqelle2jzcyqzidp8ilv+jhr2lykekb+niiingysy9spcqznojcv//ochjkrqobplgnl6xxrkgsmck//x32ugquxtorqze2tma6jjhrssjubgrf48jlxz5vxte/+otg6kdoooin0je6rhy5rc92hnvirrqo9o1f05odz4rw7h/pwilpohki/cd5/acjuklm=</latexit> <latexit sha1_base64="lwamupo1efsfsmwyisfcbrsynwi=">aaab8xicbvbns8naen3ur1q/qh69lbbbg5rebpvw9okxgrgfjjtndtmu3c2g3ylqqn+gfw8qxv033vw3btsctpxbwoo9gwbmxzngblz326msrk6tb1q3a1vbo7t79f2dr6nytzlplvc6gxpdbe+zdxwe62aaerkl1olht1o/88s04sp9ghhgikkgku84jwclijt5gq4nh0jsqzfcpjsdxizesrqorltx/wr7iuaspuafmsbw3ayigmjgvlbjlcwnywgdkqelle2jzcyqzidp8ilv+jhr2lykekb+niiingysy9spcqznojcv//ochjkrqobplgnl6xxrkgsmck//x32ugquxtorqze2tma6jjhrssjubgrf48jlxz5vxte/+otg6kdoooin0je6rhy5rc92hnvirrqo9o1f05odz4rw7h/pwilpohki/cd5/acjuklm=</latexit> Gaussian Naïve Bayes P (x i Y c )= 1 q2 exp 2 Yc (xi µ Yc ) Y c, Y c X i - 특정에대한의평균, 표준편차를에대입 µ,
66 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> Gaussian Naïve Bayes P (x i Y c )= 1 q2 exp 2 Yc (xi µ Yc ) Y c,
67 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> Gaussian Naïve Bayes P (x i Y c )= 1 q2 exp 2 Yc (xi µ Yc ) Y c,
68 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> Gaussian Naïve Bayes P (x i Y c )= 1 q2 exp 2 Yc (xi µ Yc ) Y c,
69 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">aaacyxicbvfnaxsxenvukzz102sthnmrmqubernrcm0phdbeenshzgews2jlwvte2t1ksyfg7j/sladc+kmq23tokg4ihm/em5geskpji3f8h4qvtrzfvtp53xmz+3zvpzo4pldlbqsmralkc5lxc0owmeajci4ra1xnci6ym2+r/sutgcvl4icuk5hqpi9klgvht6xrcts7s51sknnyrq9s0adfkmsnfy5phlo/dlohqyszcq759tb1xtm0xg93fvoqy+90i2/nnppj9ubvvx423vzyyyycl7b/0kmjbjyi10wfg6qfxdlwki1+s1kpag0fcswtnsrxhvphduqhoomw2klfxq2fw8tdgmuwu7eoqkhvptojewn8kzcu2x8djmtrlzrzss1xyz/2vut/epma809tj4uqrijezlfek4olxevnz9kaqlx0gasj/v2pwhafgppfwywqph3yczaedj4pkh8fumdf2zr2ybe5jj2ski/kjhwnizimgjwe28fesb/8ctthfb5upghqet6rrxue/qwuvbzr</latexit> P (x i Y c )= Gaussian Naïve Bayes 1 q2 exp 2 Yc (xi µ Yc ) Y c,
70 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">aaacyxicbvfnaxsxenvukzz102sthnmrmqubernrcm0phdbeenshzgews2jlwvte2t1ksyfg7j/sladc+kmq23tokg4ihm/em5geskpji3f8h4qvtrzfvtp53xmz+3zvpzo4pldlbqsmralkc5lxc0owmeajci4ra1xnci6ym2+r/sutgcvl4icuk5hqpi9klgvht6xrcts7s51sknnyrq9s0adfkmsnfy5phlo/dlohqyszcq759tb1xtm0xg93fvoqy+90i2/nnppj9ubvvx423vzyyyycl7b/0kmjbjyi10wfg6qfxdlwki1+s1kpag0fcswtnsrxhvphduqhoomw2klfxq2fw8tdgmuwu7eoqkhvptojewn8kzcu2x8djmtrlzrzss1xyz/2vut/epma809tj4uqrijezlfek4olxevnz9kaqlx0gasj/v2pwhafgppfwywqph3yczaedj4pkh8fumdf2zr2ybe5jj2ski/kjhwnizimgjwe28fesb/8ctthfb5upghqet6rrxue/qwuvbzr</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> P (x i Y c )= Gaussian Naïve Bayes 1 q2 exp 2 Yc (xi µ Yc ) Y c,
71 <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> <latexit sha1_base64="qmslbdtknmcrkvhn/lewslhfxwi=">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</latexit> P (Y c X 1,...,X n )= P (x i Y c )= P (Y c ) n Q i=1 nq i=1 P (X i Y c ) P (X i ) 1 q2 exp 2 Yc Y c is a label (xi µ Yc ) Y c
72 Human knowledge belongs to the world.
73 Naïve Bayes with Sklearn Naive Bayes Classifier Director of TEAMLAB Sungchul Choi
74 CountVectorizer in Scikit-learn - 문서에서 Bag of Words Vector 를뽑아주는 class action.text.countvectorizer.html
75 CountVectorizer in Scikit-learn - 다른전처리모듈처럼생성 à 적용의과정을거침
76 CountVectorizer in Scikit-learn - 다른전처리모듈처럼생성 à 적용의과정을거침
77 NB classifier family in scikit-learn - Scikit-learn에서제공하는 NB classifier - Bernoulli Naïve Bayes - Multinomial Naïve Bayes - Gaussian Naïve Bayes
78 Bernoulli Naïve Bayes
79 Bernoulli Naïve Bayes
80 Multinomial Naïve Bayes
81
82 Gaussian Naïve Bayes
83
84 Human knowledge belongs to the world.
Lecture12_Bayesian_Decision_Thoery
Bayesian Decision Theory Jeonghun Yoon Terms Random variable Bayes rule Classification Decision Theory Bayes classifier Conditional independence Naive Bayes Classifier Laplacian smoothing MLE / Likehood
Overview Decision Tree Director of TEAMLAB Sungchul Choi
Overview Decision Tree Director of TEAMLAB Sungchul Choi 머신러닝의학습방법들 - Gradient descent based learning - Probability theory based learning - Information theory based learning - Distance similarity based
단순 베이즈 분류기
단순베이즈분류기 박창이 서울시립대학교통계학과 박창이 ( 서울시립대학교통계학과 ) 단순베이즈분류기 1 / 14 학습내용 단순베이즈분류 구현 예제 박창이 ( 서울시립대학교통계학과 ) 단순베이즈분류기 2 / 14 단순베이즈분류 I 입력변수의값이 x = (x 1,..., x p ) 로주어졌을때 Y = k일사후확률 P(Y = k X 1 = x 1,..., X p =
cha4_ocw.hwp
제 4장 확률 우리는 일상생활에서 확률이라는 용어를 많이 접하게 된다. 확률(probability)는 한자어로 확실할 확( 確 ), 비율 률( 率 )로 해석된다. 로또당첨확률, 야구 한국시리즈에서 특정 팀이 우승 할 확률, 흡연자가 폐암에 걸릴 확률, 집값이 오를 확률 등 수없이 많은 확률들이 현대생활 에서 사용되어지고 있다. 대부분의 일간신문에는 기상예보
Multi-pass Sieve를 이용한 한국어 상호참조해결 반-자동 태깅 도구
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확률과통계 강의자료-1.hwp
1. 통계학이란? 1.1 수학적 모형 실험 또는 증명을 통하여 자연현상을 분석하기 위한 수학적인 모형 1 결정모형 (deterministic model) - 뉴톤의 운동방정식 : - 보일-샤를의 법칙 : 일정량의 기체의 부피( )는 절대 온도()에 정비례하고, 압력( )에 반비례한다. 2 확률모형 (probabilistic model) - 주사위를 던질 때
Overview Ensemble Model Director of TEAMLAB Sungchul Choi
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