狠狠撸

????? ??? ??
?????
2016. 12. 06 (?)
????? ????????
? ? ? ???
bhkim@bi.snu.ac.kr

Contents
? ????? ???? ??
? ?? ??? ??
? Part 1: ???? ?? ?? ???
? Part 2: ???? ??: Weka? ?? ?? ??
? Part 3: ??? ????: Neural Network Playground
? ??
? 2016 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/ 2

????? ???? ??

Intro
狠狠撸 by Jiqiong Qiu at DevFest 2016
http://www.slideshare.net/SfeirGroup/first-step-deep-learning-by-jiqiong-qiu-devfest-2016
????
????
(????)
???

Image source: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

????(AI)
? “???? ???? ???? ???? ??”(???, SW)? ??
? ??? ???? ? ?? ?? ??? ? ? ??? ?? ??
? ??? ??? ?? ?? ??? ? ? ??? ?? ??
? 1950: Turing’s Paper, 1956: “Artificial Intelligence (AI)”
6(KIPS 2016 Conference Keynote by Byoung-Tak Zhang)

???? ??? ??: ??
? AI ??? ??? ‘???‘ ???/??? ??? ?
? ???? ??? ????(intelligent), ??? ?? ???
???(adaptive), ???(robust)?? ??
? ??? ??? ????? ???? ?? ???
? ??? ????? ??? ??? ??? ?? ????,
??? ????? ????? ??!
? ??? AI ?? ?? ? ‘??(learning)‘ ??
cat car
(Sam Roweis, MLSS’05 Lecture Note)

??? ??? ??? ???? ?? ????
??(world)? ???? ??? ??? ??
???? ?? ? ?? ??
?? ??
?? ??
??? ??
Computer Vision
???? ??/??
Natural Language
Processing / Understanding
?? ??
Speech Recognition
+ ???? ??? ?? ?? ??? ????
Intelligent Agent
??? ????
Intelligent Agent

?? ??? ??

??? ??? ??? ???? ?? ????
???? ???? ??? ???
??(world)? ???? ??? ??? ??
+ ???? ??? ?? ??
(??) ??
(recognition)
(??) ??
(recognition)
(??) ??
(generation)
?

?? ???? ?? ???? ?? ??
(??) ??
(recognition)
(??) ??
(recognition)
(??) ??
(generation)
?
?? ?? ??? ????? ??
?? ??? ??? ?? ??? ???? ??
??? ??? ???? ??? ??? ??

??? ??(data analysis)? ??
? ?? ??(define the question)
? ??? ??(dataset)
? ???? ???? ??(define the ideal data set)
? ??? ??? ??(determine what data you can access)
? ??? ??(obtain the data)
? ??? ??(clean the data)
? ??? ??? ??(exploratory data analysis)
? ???/??? ???(Clustering / Data visualization)
? (???) ??/???((statistical) prediction/modeling)
? ??/??(Classification / Prediction)
? ?? ??(interpret results)
? ??? ?? ?? ? ??(evaluation), ?? ?? ?? ??(model selection)
? ?? ?? ? ??? ?? ?? ?? ? ??(challenge results)
? ?? ?? ? ??? ??(synthesize/write up results)
? ?? ?? ??? ???? ??(create reproducible code)
(c) 2008-2015, B.-H. Kim 12
J. Leek, Data Analysis – Structure of a Data Analysis, Lecture at Coursera, 2013
?? ?? ?? ?? ??
Data mining
Data Science
Analytics
…
?????
????
?? ???
???? ??

???? & ???? ??? ?? ??
Problem &
Dataset
Tool
Process &
Algorithm
(Programming)

???? & ???? ??? ?? ?? ???
1. ?? ??
1. ?? ??? ?? ??? ??? ?? ????
2. ?? ?? ?? ? ??? ??? ?? ??? ???
3. ?? ???? ??? ??? ?? ??? ??? ??? ??
2. ??? ?? ??? ???? (??? ??)
1. Weka? ???? ?? (NO ?????!)
2. NN Playground? ??? ?? (NO ?????!)
3. ?? ??/???? ????
1. ‘??? ?? ????/??? ??’: http://hunkim.github.io/ml/
2. ??????????(http://nacsi.kr/) ???? ????

?? ?????.

????? ?? ?? ???
Part 1

Machine Learning

???? (Machine Learning, ML)
Q. If the season is dry and the pavement is slippery, did it rain?
A. Unlikely, it is more likely that the sprinkler was ON
??? ????
??? ?????
??? ??? ??? ????
????, ?? ?? ???? ??
?? ??
?? ??
??, ????
?? ?? ??
?? ?? ??
?? ?? ?? ??

????? ? ?? ?? ?? ??
? Supervised Learning (????, ????, ????)
? ???(?? ???? ??) ?? ??? ????
? ??? ?? ?? ?? ???? ??? ????
? ?) ??(classification): ???? ????(discrete) ??.
? ?) ??(regression): ???? ??? ?? ??? ??
? Unsupervised Learning (?????, ?????, ?????)
? ???? ??? ??, ??, ??? ??? ?? ??. ???? ????
??
? ?? ???? ??? ?? ?? ????? ??? D={(x)}
? ?: ?? ??(dimension reduction), ???(clustering)
? Reinforcement Learning (????)
? ???? ??? ???(right/wrong)? ?? ???? ?? ??
? ????? ?????, ??(environment) ??? ??(rewards)? ???
?? ??? ??(action)? ????? ???? ??
? ??? ??, ????? ??, ?? ?? ?? ? ??, ?? ??? ???
??
? Action selection, planning, policy learning

Img source: http://nkonst.com/machine-learning-explained-simple-words/

????(connectionism)
? ????, ???? ??? ???? ?? ?? ??(neural
information processing)? ?? ??? ?? ? ??
? ??? ??? ??? ?(network)?? ?? ??? ??? ???
??
? ?????(artificial neural networks)
? ????(??)? ??? ??? ?? ???? ?? ??? ??
?? ??
??? ??(feedforward)

???(Deep Learning)
(??: Y. Bengio, Deep Learning Summer School 2015)
? ??: ??? ??, ?? ??: ???? ???? ??
GoogLeNet
DeepFace
Convolutional NN
Stacked RBM
Neural
Machine Translator
? ?? ??? ?? ? ?? ??/??, ?? ??, ???? ??? ??,
?? ?? ? ????? ???? ???? ?? ??? ??? ??.
? (?? ??)?????? ?? ??? ??? ????
? ?? ??? ?? ??? ???? ????
?? ???? ????? ??
Deep Q Network

???? ???:
WEKA? ?? ?? ??
Part 2

[ Problem &
Dataset ]
?? ?? ??
[ Tool ]
Weka
[ Process &
Algorithm ]
????
?????
(No
Programming)

Outline
?Part 2-1: Weka ??
?Part 2-2: Weka? ?? ?? ??
?Weka ????? ??? ??/?? ????
????, ?? ??? ??? ?????
????? ???????.
(c)2008-2016, SNU Biointelligence Laboratory, http://bi.snu.ac.kr
25

Outline
?Weka Explorer? ??? ??? ? ?? ??
? Filter, Visualize
? Dataset: diabetes
?Weka Explorer? ??? Classification
? Dataset: Iris, diabetes
? Classifier: ????(ID3, J48, SimpleCart), Random Forest
?Weka Experimenter? ??? ?? ??
? Dataset: diabetes
? T-test? ??? ?? ?? ??
26

?? 1 ??
? ?? ?? ?? ????
? ??: ?? ?? ??, ??? ??
? ??: ???
? ?? ??: ??? ??/???, ?? ??
? ??: Weka? Explorer, Experimenter
? ?? ??? ? ? ???? ?? ?? ?? ??
? ??: ???
? ?? ??: ??? ??? ??
? ?? ??
? ??? ???
? (??? ?? ??? ?? ? ??)
? ??? ???
? ??: Weka? Explorer
27

Weka ??
? ???? ???? ???? ??, ??? ??? ??
? Weka? ?? ??
? ??? ???(data pre-processing), ??? ??(feature selection)
? ???(clustering), ???(visualization)
? ??(classification), ????(regression), ??? ??(forecast)
? ?? ?? ??(association rules)
? S/W ??
? ?? ? ?? ?? ?????(free & open source GNU General Public License)
? ?? analytics S/W? ??? ?: RapidMiner, MOA
? Java? ??. ??? ????? ?? ??
? Python, C, R, Matlab ? ?? ??? ?? ??
? ????
? Google?? Weka? ??, ? ?? ?? ??
? http://www.cs.waikato.ac.nz/ml/weka/
(c)2008-2016, SNU Biointelligence Lab. 28Weka (bird): http://www.arkive.org/weka/gallirallus-australis/video-au00.html

Top 20 Most Popular Tools for
Big Data, Data Mining, and Data Science
(c)2008-2016, SNU Biointelligence Lab. 29Source: http://www.kdnuggets.com/2015/06/data-mining-data-science-tools-associations.html
Red: Free/Open Source tools
Green: Commercial tools
Fuchsia: Hadoop/Big Data tools

Weka? ???? ?????
(c)2008-2016, SNU Biointelligence Lab.
30
Explorer: ??? ?? ??? ? ???
?? ?? ? ?? ?? ??.
?????, ?? ?? ??
KnowledgeFlow:
??? ?? ??? ?? ??? ????
????? ???? ?? ??
Experimenter: ?? ? ?? ???
?? ??. ?? ?? ??.
- ??? ???? ? ???? ??
- ?? ???-???? ?? ?? ??
- ?? ?? ? ??? ??
- ??? ??? ?? ??
Simple CLI: ?? ??????
????? ???? ???. Weka?
?? ??? ???? ?? ??
? ? ?? ??
Workbench: ?? ?????? ???
?? ????? (3.8.0?? ??)

??: ??(iris) ??(classification)
31
Iris virginicaIris versicolorIris setosa
??? ??? ???

??: ??(iris) ??(classification)
? ??? ??(Define features or attributes)
? Sepal length, sepal width, petal length, petal width
? ?? ??(Class label): ??(iris)? ? ??. Setosa, versicolor, ? virginica
? ?? ?? ? ??? ??
? ? ?? ?? ?? 50?? ?? ?? (1935?)
? Data table : 150 samples (or instances) * 5 attributes
? R. Fisher ?? 1936? ?? ???? ? ???? linear discriminant model ? ???
? ??: ?? ???? ?? ? ???? ??
? ? ?? ?? ?????? ??: ???, ?? ??, SVM
? ? ???? ? ???? ??? ???? ??
? ?? ?? ?? ? ?? ??
? ??? ?? ?? ??
? ?? ??? ???? ?? ?? ??(algorithm + parameter setting) ??
? ??
32

??: ??(iris) ????
? Just open “iris.arff” in ‘data’ folder
33

Weka ??? ?? (.ARFF)
@RELATION iris
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth real
@ATTRIBUTE petallength NUMERIC
@ATTRIBUTE petalwidth numeric
@ATTRIBUTE class {Iris-setosa, Iris-versicolor, Iris-virginica}
@DATA
5.1, 3.5, 1.4, 0.2, Iris-setosa
4.9, 3.0, 1.4, 0.2, Iris-setosa
4.7, 3.2, 1.3, 0.2, Iris-setosa
…
7.0, 3.2, 4.7, 1.4, Iris-versicolor
…
???
(CSV format)
??
34
Note: Excel? ???? CSV ?? ?? ?, ??? ???? ?? arff ??? ?? ?? ??
Dataset name Attribute name Attribute type

ARFF Example
35
%
% ARFF file for weather data with some numeric features
%
@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature numeric
@attribute humidity numeric
@attribute windy {true, false}
@attribute play? {yes, no}
@data
sunny, 85, 85, false, no
sunny, 80, 90, true, no
overcast, 83, 86, false, yes
...
狠狠撸 from Data Mining: Practical Machine Learning Tools and Techniques (Chapter 2)

Preprocess ??? ??? ??? ?? ??
? ??? ??(current relation)
? ??? ??(remove attributes)
? ??? ? ??? ?? ??(selected attribute)
? ?? ???? ???? class label ?? ???(Visualize All)
? ‘Filter’? ??? preprocessing
(c)2008-2016, SNU Biointelligence Lab. 36

?? ?? ?? ? ?? ?? ??
?
? ?
?
?
Data matrix
(row: instance)
(col: feature)
Feature
Selection
PCA
SVM
Decision
Tree
Neural
Networks
Accuracy +
Cross-
validation
ROC
Curve
AUC
(Area
Under ROC
Curve)
feature
instance
normalization
Dataset
??/Cleaning
Feature
Manipulation
Classification
Regression
Evaluation
Fill missing values
standardization
?? ??? ?? ??? ???? ?? ? ???
(c)2008-2016, SNU Biointelligence Laboratory, http://bi.snu.ac.kr 37

Approaching (Almost) Any Machine Learning Problem
FIGURE FROM: A. THAKUR AND A. KROHN-GRIMBERGHE, AUTOCOMPETE: A FRAMEWORK FOR MACHINE LEARNING
COMPETITIONS, AUTOML WORKSHOP, INTERNATIONAL CONFERENCE ON MACHINE LEARNING 2015.
http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/

??: ?? ??? ??? ??
? Description
? Pima Indians have the highest prevalence of diabetes in the world
? We will build classification models that diagnose if the patient shows signs of
diabetes
? http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
? Configuration of the data set
? 768 instances
? 8 attributes
? age, number of times pregnant, results of medical tests/analysis
? all numeric (integer or real-valued)
? Class label = 1 (Positive example )
? Interpreted as "tested positive for diabetes"
? 268 instances
? Class label = 0 (Negative example)
? 500 instances

WEKA? ??? ??? ???
? ??? ??(DESCRIPTIVE ANALYSIS)
Part Ⅰ

Preprocess ??? ??? ?? ??
? ??? ??(current relation)
? ??? ??(remove attributes)
? ??? ? ??? ?? ??(selected attribute)
? ?? ???? ???? class label ?? ???(Visualize All)
? ‘Filter’? ??? preprocessing: Part V?? ??

Weka - Explorer – Preprocess –Filter
? ??? ??? ???
? ?? ?? ?? ????
? Fill in missing values
? weka.filters.unsupervised.attribute.ReplaceMissingValues ??
? Standardization for all the attributes: x? z ? ??
? weka.filters.unsupervised.attribute.Standardize ??
? Data reduction using PCA
? weka.filters.unsupervised.attribute.PrincipalComponents ??
? ???? ? maximumAttributes? 10~50 ?? ? ???
??? ??? ??
? Check the effect of PCA using ‘Visualize-Plot Matrix’ ??? ?
(c)2008-2016, SNU Biointelligence
Laboratory, http://bi.snu.ac.kr
42

Weka - Explorer – Visualize ? ???
??? ?? (descriptive analysis)
? Check the effect of PCA using ‘Visualize-Plot Matrix’
? PCA ?? ?/? ? plot matrix? ???? PCA? ???
???? ??
? PCA ?? ?(401?? ???) ? ?(???? ??? ?? ???
???)? ?? Plot Matrix ??? ?? ????, ?? ? ???
??

WEKA EXPLORER? ???
CLASSIFICATION
Part Ⅱ

45
?? ???? – ????(Decision Trees)
?J48 (C4.5? Java ?? ??)
? ?? ?? ???? ?? ??? ‘??’ ??? ?? ? ??
? Weka?? ????: classifiers-trees-J48

46
??: ???(Neural Networks)
?MLP (Multilayer Perceptron)
? ????? ?? ??? ??? ??? ?? ????
? Weka?? ????: classifiers-functions-MultilayerPerceptron
(c)2008-2016, SNU Biointelligence
Laboratory, http://bi.snu.ac.kr
Figure from Andrew Ng’s Machine Learning Lecture Notes, on Coursera, 2013-1

Weka? ?? ???? – ???? ?
47
click ? load a file that contains the
training data by clicking
‘Open file’ button
? ‘ARFF’ or ‘CSV’ formats are
readable
? Click ‘Classify’ tab
? Click ‘Choose’ button
? Select ‘weka – function
- MultilayerPerceptron
? Click ‘MultilayerPerceptron’
? Set parameters for MLP
? Set parameters for Test
? Click ‘Start’ for learning

48
?? ????? ???? ??
? ???? ??(Parameter Setting) = ??? ??(Car Tuning)
? ?? ?? ?? ???? ??
? ???? ??? ?? ??? ??????? ???? ??? ???
?? ?? ?? ??
? ????? ?? ???? (J48, SimpleCart in Weka)
? ??? ??? ??? ??? ?? ????: confidenceFactor, pruning,
minNumObj ?
? Random Forest? ?? ???? (RandomForest in Weka)
? numTrees: ?? ? ??? ??? tree? ?? ??. ??? ?? ??
???, overfitting? ???? ??.
? ??: ???? ?? ???? (MultilayerPerceptron in Weka)
? ?? ??: hiddenLayers,
? ?? ?? ??: learningRate, momentum, trainingTime (epoch), seed

Test Options and Classifier Output
49(c)2008-2016, SNU Biointelligence Laboratory, http://bi.snu.ac.kr
There are
various metrics
for evaluation
Setting the
data set used
for evaluation

Classifier Output
?Run information
?Classifier model (full
training set)
?Evaluation results
? General summary
? Detailed accuracy by
class
? Confusion matrix
The output
depends on
the classifier

????? (ANN) ??
? ANN? ?? ???? ??(functions-MultilayerPerceptron ??)
? learningRate -- The amount the weights are updated.
? momentum -- Momentum applied to the weights during updating.
? hiddenLayers –
? This defines the hidden layers of the neural network. This is a list of positive whole
numbers. 1 for each hidden layer. Comma seperated.
? Ex) 3: one hidden layer with 3 hidden nodes
? Ex) 5,3; two hidden layers with 5 and 3 hidden nodes, respectively
? To have no hidden layers put a single 0 here. This will only be used if autobuild is
set. There are also wildcard values 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' =
classes , 't' = attribs + classes.
? trainingTime -- The number of epochs to train through. If the validation set is
non-zero then it can terminate the network early
? Experiments
? ?? ??: ?? ????? ??? ???? ?? ???? ?? ???? ??
?? ??

SVM ??
? SVM? ?? ???? ??(functions-SMO ??)
? c -- The complexity parameter C.
? kernel -- The kernel to use.
? PolyKernel -- The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x,
y>+1)^p.
? “exponent” represents p in the equations.
? RBFKernel -- K(x, y) = e^-(gamma * <x-y, x-y>^2)
? gamma (γ) controls the width (range of neighborhood) of the kernel
? Experiments
? ?? ??: ??? ?? ??. ??? ?? ???? ?? ??
? PolyKernel: testing several exponents. {1, 2, 5}
? RBF kernel: “grid-search" on C and γ using cross-validation.
? C = {0.1, 1, 10}, γ = {0.1, 1, 10}
? Reference
? A practical guide to SVM classification (http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf)

WEKA EXPERIMENTER?
??? CLASSIFICATION
Part Ⅲ

Using Experimenter in Weka
? Tool for ‘Batch’ experiments
54
click
? Set experiment type/iteration
control
? Set datasets / algorithms
Click ‘New’
? Select ‘Run’ tab and click ‘Start’
? If it has finished successfully, click
‘Analyse’ tab and see the summary

Usages of the Experimenter
? Model selection for classification/regression
? Various approaches
? Repeated training/test set split
? Repeated cross-validation (c.f. double cross-validation)
? Averaging
? Comparison between models / algorithms
? Paired t-test
? On various metrics: accuracies / RMSE / etc.
? Batch and/or Distributed processing
? Load/save experiment settings
? http://weka.wikispaces.com/Remote+Experiment
? Multi-core support : utilize all the cores on a multi-core machine

Experimenter ??

Experimenter ??
? ?? ?? ?? ??(Analyse ??? 1~4 ???? ??)
(C) 2014-2015, B.-H Kim 58
? Accuracy: percent_correct ??
? F1-measure: F_measure ??
? ROC Area: Area Under ROC ??
1
2
3
4

??: Package Manager
? Explorer? ??? ?? ?? ??

??? ????:
NEURAL NETWORK PLAYGROUND
Part 3

[ Problem &
Dataset ]
?? ?? ??
[ Tool ]
Web Demo
[ Process &
Algorithm ]
?????
(No
Programming)

Neural Network ??
(c)2008-2016, SNU Biointelligence Lab. 62
http://playground.tensorflow.org/
??, ??? ??: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground

Neural Network Activation Functions
(A. Graves, 2012)
???? ? = max(?, 0)
Rectified Linear Unit
[??]
? Hidden unit? sparsity? ????
? Gradient vanishing ??? ??
? ??? ???? ??? ????
?? ???? ??

??
? Data Mining, Data Science & Machine Learning

DATA MINING,
DATA SCIENCE &
MACHINE LEARNING
(c) 2008-2015, B.-H. Kim 66

What is Data?
?‘data’? ??
(c) 2008-2015, B.-H. Kim
67
“Data is a set of values of qualitative or quantitative
variables, belonging to a set of items.”
Variables: A measurement or characteristic of an item.
Qualitative: Country of origin, gender, treatment
Quantitative: Height, weight, blood pressure

What Is Data Mining?
? Data mining (knowledge discovery from data)
? Extraction of ‘interesting’ patterns or knowledge from huge
amount of data
? ‘Interesting’ means: non-trivial, implicit, previously unknown
and potentially useful
? Alternative names
? Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data dredging,
information harvesting, business intelligence, etc.
? Watch out: Is everything “data mining”?
? Simple search and query processing
? (Deductive) expert systems
68狠狠撸 from Lecture 狠狠撸 of Ch. 1 by J. Han, et al., for Data Mining: Concepts and Techniques
(c) 2008-2015, B.-H. Kim

Related Research Fields
(c) 2008-2015, B.-H. Kim
Data
Mining
Artificial
Intelligence
(AI)
Machine
Learning
(ML)
Deep
Learning
Data
Science
Information
Retrieval
(IR)
Knowledge
Discovery
from Data
(KDD)
Big Data
Analytics
Business
Intelligence
69

Machine Learning & Data Mining
(c) 2008-2015, B.-H. Kim
70
狠狠撸 from GECCO 2009 Tutorial on ‘Large Scale Data Mining using
Genetics-Based Machine Learning’, by Jaume Bacardit and Xavier Llorà

Data Science
? ??? ????
? ??? ??
?? ?? ?
?? ???
???? ??
??? ??
??
(c) 2008-2015, B.-H. Kim
71

Data Science
(c) 2008-2015, B.-H. Kim
72
Figure source: http://nirvacana.com/thoughts/becoming-a-data-scientist/

??? ??? ?? ??? ??? ??
? ???(descriptive)
? Describe a set of data
? ???(exploratory)
? Find relationships you didn't know about
? Correlation does not imply causation
? ???(inferential)
? Use a relatively small sample of data to say something about a bigger population
? Inference is commonly the goal of statistical models
? ???(predictive)
? To use the data on some objects to predict values for another object
? Accurate prediction depends heavily on measuring the right variables
? ???(causal)
? To find out what happens to one variable when you make another variable change
? ????(mechanistic)
? Understand the exact changes in variables that lead to changes in other variables for
individual objects
(c) 2008-2015, B.-H. Kim
73

?? ??? ?? ???? ??
? ???(descriptive)
? ?? ?? ??(a whole population)
? ???(exploratory)
? ??? ?? ? ??? ?? ??(a random sample with many variables
measured)
? ???(inferential)
? ???? ??? ?? ? ??? ??(the right population, randomly
sampled)
? ???(predictive)
? ??? ????? ?? ???? ??? ??? ??(a training and test
data set from the same population)
? ???(causal)
? ???? ??? ??? ???? ??? ??(data from a randomized
study)
? ????(mechanistic)
? ???? ?? ??? ???? ??? ??(data about all components of
the system)(c) 2008-2015, B.-H. Kim
74

??? ??(data analysis)? ??
? ?? ??(define the question)
? ??? ??(dataset)
? ???? ???? ??(define the ideal data set)
? ??? ??? ??(determine what data you can access)
? ??? ??(obtain the data)
? ??? ??(clean the data)
? ??? ??? ??(exploratory data analysis)
? ?????/??? ???(Clustering / Data visualization)
? ??? ??/???(statistical prediction/modeling)
? ??/??(Classification / Prediction)
? ?? ??(interpret results)
? ??? ?? ?? ? ??(evaluation), ?? ?? ?? ??(model selection)
? ?? ?? ? ??? ?? ?? ?? ? ??(challenge results)
? ?? ?? ? ??? ??(synthesize/write up results)
? ?? ?? ??? ???? ??(create reproducible code)
(c) 2008-2015, B.-H. Kim
75

Raw versus processed data
?Raw data
? The original source of the data
? Often hard to use for data analyses
? Data analysis includes processing
? Raw data may only need to be processed once
?Processed data
? Data that is ready for analysis
? Processing can include merging, subsetting, transforming,
etc.
? There may be standards for processing
? All steps should be recorded
(c) 2008-2015, B.-H. Kim
76

Raw versus processed data
?Raw data ?Processed data
(c) 2008-2015, B.-H. Kim 77

狠狠撸

????? ??? ??

More Related Content

????? ??? ??