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MANG 6554 Advanced Analytics
Creation date：2024-05-08 17:35:26
Belonging category：统计学statistics
Advanced Analytics

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MANG 6554

Advanced Analytics

• Gain a basic understanding of Bayesian theorem
• Get familiar with Bayesian classifier (Naïve Bayes and Bayesian belief
networks)
• Develop a philosophical understanding of Bayesian inference and its
relation to statistical model formulations.
LL Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to data mining. Pearson Education India,
2016.
3
Bayes Classifier
A probabilistic framework for solving classification problems
Conditional Probability:
Bayes theorem:
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LL
4
Example of Bayes Theorem
Given:
• A salesperson knows that if a customer has already brought a keyboard, 50%
of the chance he/she will buy a mouse.
• Prior probability of any customer purchasing a keyboard is 1/50
• Prior probability of any customer purchasing a mouse is 1/20
If a customer has brought a mouse, what’s the probability
he/she buys a keyboard?
2.0
20/1
50/15.0
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LL
5
Using Bayes Theorem for Classification
Consider each attribute and class label as random variables
Given a record with attributes (X1, X2,…, Xd)
• Goal is to predict class Y
• Specifically, we want to find the value of Y that maximizes P(Y| X1,
X2,…, Xd )
Can we estimate P(Y| X1, X2,…, Xd ) directly from data?
LL
6
Example Data
120K)IncomeDivorced,No,Refund( ===X
Given a Test Record:
 Can we estimate
P(Evade = Yes | X) and P(Evade = No | X)?
In the following we will replace
Evade = Yes by Yes, and
Evade = No by No
Id Refund Marital
Status
Taxable
income
(K)
Evade (Y)
1 Yes Single 125 No
2 No Married 100 No
3 No Single 70 No
4 Yes Married 120 No
5 No Divorced 95 Yes
6 No Married 60 No
7 Yes Divorced 220 No
8 No Single 85 Yes
9 No Married 75 No
10 No Single 90 Yes
LL
7
Using Bayes Theorem for Classification
Approach:
• compute posterior probability P(Y | X1, X2, …, Xd) using the
Bayes theorem
• Maximum a-posteriori: Choose Y that maximizes
P(Y | X1, X2, …, Xd)
• Equivalent to choosing value of Y that maximizes
P(X1, X2, …, Xd|Y) P(Y)
How to estimate P(X1, X2, …, Xd | Y )?
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LL
8
Example Data
120K)IncomeDivorced,No,Refund( ===X
Given a Test Record:
Id Refund Marital
Status
Taxable
income
(K)
Evade (Y)
1 Yes Single 125 No
2 No Married 100 No
3 No Single 70 No
4 Yes Married 120 No
5 No Divorced 95 Yes
6 No Married 60 No
7 Yes Divorced 220 No
8 No Single 85 Yes
9 No Married 75 No
10 No Single 90 Yes
LL
9
Naïve Bayes Classifier
Assume independence among attributes Xi when class is given:
• P(X1, X2, …, Xd |Yj) = P(X1| Yj) P(X2| Yj)… P(Xd| Yj)
• Now we can estimate P(Xi| Yj) for all Xi and Yj combinations from the training
data
• New point is classified to Yj if P(Yj)  P(Xi| Yj) is maximal.
LL
10
Naïve Bayes on Example Data
120K)IncomeDivorced,No,Refund( ===X
Given a Test Record:
 P(X | Yes) =
P(Refund = No | Yes) x
P(Divorced | Yes) x
P(Income = 120K | Yes)
 P(X | No) =
P(Refund = No | No) x
P(Divorced | No) x
P(Income = 120K | No)
Id Refund Marital
Status
Taxable
income
(K)
Evade (Y)
1 Yes Single 125 No
2 No Married 100 No
3 No Single 70 No
4 Yes Married 120 No
5 No Divorced 95 Yes
6 No Married 60 No
7 Yes Divorced 220 No
8 No Single 85 Yes
9 No Married 75 No
10 No Single 90 Yes
LL
11
Estimate Probabilities from Data
Class: P(Y) = Nc/N
– e.g., P(No) = 7/10,
P(Yes) = 3/10
For categorical attributes:
P(Xi | Yk) = |Xik|/ Nc
– where |Xik| is number of
instances having attribute
value Xi and belonging to
class Yk
– Examples:
P(Status=Married|No) = 4/7
P(Refund=Yes|Yes)=0
Id Refund Marital
Status
Taxable
income
(K)
Evade (Y)
1 Yes Single 125 No
2 No Married 100 No
3 No Single 70 No
4 Yes Married 120 No
5 No Divorced 95 Yes
6 No Married 60 No
7 Yes Divorced 220 No
8 No Single 85 Yes
9 No Married 75 No
10 No Single 90 Yes
LL
12
Estimate Probabilities from Data
For continuous attributes:
– Discretization: Partition the range into bins:
Replace continuous value with bin value
Attribute changed from continuous to ordinal
– Probability density estimation:
Assume attribute follows a normal distribution
Use data to estimate parameters of distribution
(e.g., mean and standard deviation)
Once probability distribution is known, use it to estimate the conditional probability P(Xi|Y)
LL
Case category
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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