Barga Data Science lecture 6

Deriving Knowledge from Data at Scale


Feature extraction and selection are the most important but underrated step

of machine learning. Better features are better than better algorithms…




Lecture Objectives

homework

There is an order or workflow

that takes place here, don’t lose

the forest in the trees…


Review…


• Cluster 0 – It contains a cluster of Females with an average age of 37 who live in inner city and possess saving account number and current account number. They are unmarried and do not have any mortgage or pep. The average monthly income is 23,300.

• Cluster 1 - It contains a cluster of Females with an average age of 44 who live in rural area and possess saving account number and current account number. They are married and do not have any mortgage or pep. The average monthly income is 27,772.

• Cluster 2 - It contains a cluster of Females with an average age of 48 who live in inner city and possess current account number but no saving account number. They are unmarried and do not have mortgage but do have pep. The average monthly income is 27,668.

• Cluster 3 - It contains a cluster of Females with an average age of 39 who live in town and possess saving account number and current account number. They are married and do not have any mortgage or pep. The average monthly income is 24,047.

• Cluster 4 - It contains a cluster of Males with an average age of 39 who live in inner city and possess current account number but no saving account number. They are married and have mortgage and pep. The average monthly income is 26,359.

• Cluster 5 - It contains a cluster of Males with an average age of 47 who live in inner city and possess saving account number and current account number. They are unmarried and do not have mortgage but do have pep. The average monthly income is 35,419.



Classifiers Lazy –> IBk






Deriving Knowledge from Data at Scale15


No Prob Target CustID Age

1 0.97 Y 1746 …

2 0.95 N 1024 …

3 0.94 Y 2478 …

4 0.93 Y 3820 …

5 0.92 N 4897 …

… … … …

99 0.11 N 2734 …

100 0.06 N 2422

Use a model to assign score (probability) to each instance

Sort instances by decreasing score

Expect more targets (hits) near the top of the list

3 hits in top 5% of

the list

If there 15 targets

overall, then top 5

has 3/15=20% of

targets


40% of responses for

10% of cost

Lift factor = 4

80% of responses for

40% of cost

Lift factor = 2Model

Random






to impact…

1. Build our predictive model in WEKA Explorer;

2. Use our model to score (predict) which new customers to target in our upcoming advertising campaign;• ARFF file manipulation (hacking), all too common pita…• Excel manipulation to join model output with our customers list

3. Compute the lift chart to assess business impact of our predictive model on the advertising campaign• How are Lift charts built, of all the charts and/or performance

measures from a model this one is ‘on you’ to construct;• Where is the business ‘bang for the buck’?





You can’t turn data lead into modeling gold – we’re data scientists, not data alchemists…


Motivation: Real world examplesExample (1)

Lesson: Correct data transformation is important!


Motivation: Real world examplesExample (2): KDD Cup 2001

Lesson: A model that uses lots of features can turn out to be

very sub-optimal, however well it is designed!



Lesson: Feature selection can be crucial even when the

number of features is small!



Lesson: Variations of the same ML method can give vastly

different performances!



Predictive modeling competitions

Photo by mikebaird, www.flickr.com/photos/mikebaird


Global competitions

1½ weeks 70.8%

Competition closes 77%

State of the art 70%

Predicting HIV viral load

Improved by 10%


Mismatch between those with data and

those with the skills to analyse it

Crowdsourcing


Forecast Error

(MASE)

Existing model

Tourism Forecasting Competition

Aug 9 2 weeks

later

1 month

later

Competition

End


• neural networks

• logistic regression

• support vector machine

• decision trees

• ensemble methods

• adaBoost

• Bayesian networks

• genetic algorithms

• random forest

• Monte Carlo methods

• principal component analysis

• Kalman filter

• evolutionary fuzzy modeling

Users apply different techniques


VicRoads has an algorithm they use to forecast travel time on Melbourne freeways (taking into

account time, weather, accidents, etc). Their current model is inaccurate and somewhat

useless. They want to do better (or at least find out about whether it’s possible to do better).


1 2 3

Upload Submit Evaluate &

Exchange


Use the wizard to post a competition


Participants make their entries


Competitions are judged based on predictive accuracy


Competition Mechanics

Competitions are judged on objective criteria


KaggleHow They Won It…




Three Filesford_train

• 510 Trials, ~1,200 observations each spaced by 0.1 sec -> 604,330 rows

ford_test

• 100 Trials,~1,200 observations/trial, 120,841 rows

example_submission.csv


Junpei Komiyama (#4)


Junpei Komiyama (#4)


Mick Wagner (#2)


Mick Wagner (#2)


Inference (#1)


VicRoads has an algorithm they use to forecast travel time on Melbourne freeways (taking into

account time, weather, accidents etc). Their current model is inaccurate and somewhat useless.

They want to do better (or at least find out about whether it’s possible to do better).



François GUILLEM (#14)


#1 used Random Forests



Homework Week 6

Monday Sept. 21st

Upload to site…

http://blog.kaggle.com/category/dojo/Content is 10 pages of interview on how the team(s) built their models, some have multiple interviews;

You will review at least 10 interviews, bounce around do not go sequentially.

1) What model(s) did they use, 2) insights they had that influenced modeling, 3) what feature creation and

selection, 4) other observations. I will cons all these together and upload as shared document on our site.

http://blog.kaggle.com/category/dojo/


5 Minute Break…


Course Project



https://www.kaggle.com/c/springleaf-marketing-response

not

Determine whether to send a direct mail piece to a customer


The Data


The Rules





what is the data telling you




Data Wrangling


Data

Acquisition

Data

Exploration

Pre-

processing

Feature and

Target

construction

Train/ Test

split

Feature

selection

Model

training

Model

scoring

Model

scoring

Evaluation

Evaluation

Compare

metrics


• Data preparation step is by far the most time consuming step

0

10

20

30

40

50

60

70

Understanding

of Domain

Understanding

of Data

Preparation of

Data

Data Mining Evaluation of

Results

Deployment of

Results

KDDM steps

relative effort [%] Cabena et al. estimates

Shearer estimatesCios and Kurgan estimates


Out of Class Reading, highly recommended


Out of Class Reading, highly recommended


1. Do you have domain knowledge?

2. Are your features commensurate?

3. Do you suspect interdependence of features?

4. Do you need to prune the input variables

5. Do you need to assess features individually

6. Do you need a predictor?

7. Do you suspect your data is “dirty”

8. Do you know what to try first?

9. Do you have new ideas, time, computational resources, and enough examples?

10. Do you want a stable solution




Deriving Knowledge from Data at Scale15 15

𝑃 = 0.5𝑃 = 0.5


15 157 13

𝑃 = 0.5𝑃 = 0.5

𝑃 = 0.35𝑃 = 0.65

Deriving Knowledge from Data at Scale15 1515 15

𝑃 = 0.5𝑃 = 0.510 10

Deriving Knowledge from Data at Scale15 1515 15

𝑃 = 0.5𝑃 = 0.5

Time

T

r

a

i

n

T

e

s

t

Horizontal

Vertical


Data Characterization…


1. Unique values

2. Most frequent values

3. Highest and lowest values

4. Location and dispersion – gini, statistical test for dispersion

5. Quartiles


1. Missing values

2. Outliers

3. Coding

4. Constraints


Missing values – UCI machine learning repository, 31 of 68 data sets

reported to have missing values. “Missing” can mean many things…

MAR: "Missing at Random":– usually best case

– usually not true

Non-randomly missing

Presumed normal, so not measured

Causally missing

– attribute value is missing because of other attribute values (or because of

the outcome value!)





Outliers – may indicate ‘bad data’ or it may represent

something scientifically interesting in the data…

Simple working definition: an outlier is an element of a data sequence

S that is inconsistent with expectations, based on the majority of other

elements of S.

Sources of outliers

• Measurement errors

• Other uninteresting anomalous data

• Surprising observations that may be important




Simple working definition: an outlier is an element of a data sequence

S that is inconsistent with expectations, based on the majority of other

elements of S.

Sources of outliers• Insurance company sees niche of sports car enthusiasts, married boomers

with kids and second family car. Low risk, lower rate to attract. Simple case

where outlier carries meaning for modeling…


Outliers can distort the regression results. When an outlier is

included in the analysis, it pulls the regression line towards

itself. This can result in a solution that is more accurate for the

outlier, but less accurate for all the other cases in the data set.




Identify outliers• Question origin, domain knowledge invaluable

• Dispersion – "spread" of a data set, departure from central tendency, use a box plot…

Deal with outliers• Winsorize – Set all outliers to a specified percentile of the data. Not

equivalent to trimming, which simply excludes data. In a Winsorizedestimator, extreme values are instead replaced by certain percentiles (thetrimmed minimum and maximum). Same as clipping in signal processing.




Identify outliers• Question origin, domain knowledge invaluable

• Dispersion – "spread" of a data set, departure from central tendency, use a box plot…

Deal with outliers• Include – Robust statistics, a convenient way to summarize results when

they include a small proportion of outliers. A hot topic for research, seeNIPS 2010 Workshop, Robust Statistical learning (robustml).




• Entity integrity

• Referential integrity

• Type checking

• Format

• Bounds checking

Constraints


• weka.filters.unsupervised.instance.RemoveMisclassified

• weka.filters.unsupervised.instance.RemovePercentage

• weka.filters.unsupervised.instance.RemoveRange

• weka.filters.unsupervised.instance.RemoveWithValues

• weka.filters.unsupervised.instance.Resample


5 Minute Break…


Simple Definition

feature selection problem

Feature extraction

11 .{ ,..., ,..., } { ,..., ,..., }

j mi n i i if selectionf f f f f f

F

F‘ F F‘

1 1 1 1 1.{ ,..., ,..., } { ( ,..., ),..., ( ,..., ),..., ( ,..., )}i n n j n m nf extraction

f f f g f f g f f g f f



3 types of methodsFilter Methods

Wrapper Methods

Embedded Methodsdecision trees, random forests


Most learning methods implicitly do feature selection:• Decision Trees: use info gain or gain ratio to decide what attributes to use as

tests. Many features don’t get used.

• neural nets: backprop learns strong connections to some inputs, and near-

zero connections to other inputs.

• kNN, MBL (any similarity based learning): weights in Weighted Euclidean

Distance determine how important each feature is. Weights near zero mean

feature is not used.

• SVMs: maximum margin hyperplane may focus on important features,

ignore irrelevant features.

So why do we need feature selection?

Data Integration


Curse of Dimensionality

exponentially

In many cases the information lost by

discarding variables is made up for by a

more accurate mapping/sampling in the

lower-dimensional space !


Feature Selection and EngineeringOptimality?

This deserves a deeper treatment, which we will cover next week with hands-on exercises in class…


Numerical data• Binning – a mapping to discrete categories;

• Recenter – shift by c where max, min, avg and median shift, the range and

standard deviation will not shift;

• Rescale – multiply everything by d, all measures change;

• Standard ND – recenter, make mean 0, divide all previous values by SD

Character data

• Lower case

• Spellcheck

• Data extraction (e.g. regular expressions)

Coding – shape and enrich…



feature

red

blue

green

red

red

green

blue

red blue green

1 0 0

0 1 0

0 0 1

1 0 0

1 0 0

0 0 1

0 1 0


Outlook T empera ture Humidity Windy Play

sunny 85 85 false no

sunny 80 90 true no

overcast 83 78 false yes

rain 70 96 false yes


rain 65 70 true no

overcast 64 65 true yes

sunny 72 95 false no

sunny 69 70 false yes


sunny 75 70 true yes

overcast 72 90 true yes

overcast 81 75 false yes

rain 71 80 true no

Attributes:

Outlook (overcast, rain, sunny)

Temperature real

Humidity real

Windy (true, false)

Play (yes, no)

OutLook OutLook OutLook Temp Humidity Windy Windy Play Play

overcast rain sunny TRUE FALSE yes no

0 0 1 85 85 0 1 1 0

0 0 1 80 90 1 0 0 1

1 0 0 83 78 0 1 1 0

0 1 0 70 96 0 1 1 0

0 1 0 68 80 0 1 1 0

0 1 0 65 70 1 0 0 1

1 0 0 64 65 1 0 1 0

. . . . . . . . .

. . . . . . . . .

Standard

Spreadsheet

Format




Household income

$10.000 $200.000

verylow

low average high veryhigh


Less features, more discrimination ability

concept hierarchies


• Equal-width (distance) partitioning

uniform grid

• Equal-depth (frequency) partitioning

• Class label based partitioning


into the user-specified


[64,67) [67,70) [70,73) [73,76) [76,79) [79,82) [82,85]

Temperature values:

64 65 68 69 70 71 72 72 75 75 80 81 83 85

2 2

Count

4

2 2 20


[0 – 200,000) … ….

1

Count

Salary in a corporation

[1,800,000 –

2,000,000]


user-specified nFi number of intervals


[64 .. .. .. .. 69] [70 .. 72] [73 .. .. .. .. .. .. .. .. 81] [83 .. 85]

Temperature values:

64 65 68 69 70 71 72 72 75 75 80 81 83 85

4

Count

4 4

2



4/12/2016 University of Waikato 119











































Domain expertise, play a hunch in terms of feature discrimination


That’s all for tonight….

Barga Data Science lecture 6

Data & Analytics