MACHINE LEARNING FROM

DISASTERF#unctional Londoners @ Skills Matter

Phil Trelford 2013 @ptrelford

RMS TitanicOn April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. 

…there were not enough lifeboats for the passengers and crew. 

…some groups of people were more likely to survive than others, such as women, children, and the upper-class.

Kaggle competition

Kaggle Titanic datasettrain.csv

test.csv

PassengerIdSurvived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked1 0 3 Braund, Mr. Owen Harrismale 22 1 0 A/5 21171 7.25 S2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer)female 38 1 0 PC 17599 71.2833 C85 C3 1 3 Heikkinen, Miss. Lainafemale 26 0 0 STON/O2. 3101282 7.925 S4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel)female 35 1 0 113803 53.1 C123 S5 0 3 Allen, Mr. William Henrymale 35 0 0 373450 8.05 S6 0 3 Moran, Mr. Jamesmale 0 0 330877 8.4583 Q7 0 1 McCarthy, Mr. Timothy Jmale 54 0 0 17463 51.8625 E46 S8 0 3 Palsson, Master. Gosta Leonardmale 2 3 1 349909 21.075 S9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female 27 0 2 347742 11.1333 S

10 1 2 Nasser, Mrs. Nicholas (Adele Achem)female 14 1 0 237736 30.0708 C11 1 3 Sandstrom, Miss. Marguerite Rutfemale 4 1 1 PP 9549 16.7 G6 S12 1 1 Bonnell, Miss. Elizabethfemale 58 0 0 113783 26.55 C103 S13 0 3 Saundercock, Mr. William Henrymale 20 0 0 A/5. 2151 8.05 S14 0 3 Andersson, Mr. Anders Johanmale 39 1 5 347082 31.275 S15 0 3 Vestrom, Miss. Hulda Amanda Adolfinafemale 14 0 0 350406 7.8542 S16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16 S17 0 3 Rice, Master. Eugenemale 2 4 1 382652 29.125 Q18 1 2 Williams, Mr. Charles Eugenemale 0 0 244373 13 S19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)female 31 1 0 345763 18 S20 1 3 Masselmani, Mrs. Fatimafemale 0 0 2649 7.225 C21 0 2 Fynney, Mr. Joseph Jmale 35 0 0 239865 26 S22 1 2 Beesley, Mr. Lawrencemale 34 0 0 248698 13 D56 S23 1 3 McGowan, Miss. Anna "Annie"female 15 0 0 330923 8.0292 Q24 1 1 Sloper, Mr. William Thompsonmale 28 0 0 113788 35.5 A6 S25 0 3 Palsson, Miss. Torborg Danirafemale 8 3 1 349909 21.075 S26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)female 38 1 5 347077 31.3875 S27 0 3 Emir, Mr. Farred Chehabmale 0 0 2631 7.225 C28 0 1 Fortune, Mr. Charles Alexandermale 19 3 2 19950 263 C23 C25 C27 S

DATA ANALYSISTitanic: Titanic: Machine Learning from Disaster

FSharp.Data: CSV Provider

Countinglet female (passenger:Passenger) = passenger.Sex = “female”

let survived (passenger:Passenger) = passenger.Survived = 1

let females = passengers |> where female

let femaleSurvivors = females |> tally survived

let femaleSurvivorsPc = females |> percentage survived

Tally Ho!/// Tally up items that match specified criteria

let tally criteria items =

items |> Array.filter criteria |> Array.length

/// Percentage of items that match specified criteria

let percentage criteria items =

let total = items |> Array.length

let count = items |> tally criteria

float count * 100.0 / float total

Survival rate/// Survival rate of a criteria’s group

let survivalRate criteria =

passengers |> Array.groupBy criteria

|> Array.map (fun (key,matching) ->

key, matching |> Array.percentage survived

)

let embarked = survivalRate (fun p -> p.Embarked)

Score

let score f = passengers |> Array.percentage (fun p -> f p = p.Survived)

let rate = score (fun p -> (child p || female p) && not (p.Class = 3))

MACHINE LEARNING

Titanic: Machine Learning from Disaster

20 QuestionsThe game suggests that the information (as measured by Shannon's entropy statistic) required to identify an arbitrary object is at most 20 bits. The game is often used as an example when teaching people about information theory. Mathematically, if each question is structured to eliminate half the objects, 20 questions will allow the questioner to distinguish between 220 or 1,048,576 objects.

Decision TreesA tree can be "learned" by splitting the source set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called recursive partitioning.

Split data set (from ML in Action)

Python

def splitDataSet(dataSet, axis, value):

retDataSet = []

for featVec in dataSet:

if featVec[axis] == value:

reducedFeatVec = featVec[:axis]

reducedFeatVec.extend(featVec[axis+1:])

retDataSet.append(reducedFeatVec)

return retDataSet

F#

let splitDataSet(dataSet, axis, value) =

[|for featVec in dataSet do

if featVec.[axis] = value then

yield featVec |> Array.removeAt axis|]

Decision Tree

let labels =

[|"sex"; "class"|]

let features (p:Passenger) : obj[] =

[|p.Sex; p.Pclass|]

let dataSet : obj[][] =

[|for passenger in passengers ->

[|yield! features passenger;

yield box (p.Survived = 1)|] |]

let tree = createTree(dataSet, labels)

Overfitting

CLASSIFYTitanic: Machine Learning from Disaster

Decision Tree: Create -> Classifylet rec classify(inputTree, featLabels:string[], testVec:obj[]) =

match inputTree with

| Leaf(x) -> x

| Branch(s,xs) ->

let featIndex = featLabels |> Array.findIndex ((=) s)

xs |> Array.pick (fun (value,tree) ->

if testVec.[featIndex] = value

then classify(tree, featLabels,testVec) |> Some

else None

)

Titanic Data

Variable Description

survival Survival (0 = No; 1 = Yes)

pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)

name Name

sex Sex

age Age

sibsp Number of Siblings/Spouses Aboard

parch Number of Parents/Children Aboard

ticket Ticket Number

fare Passenger Fare

cabin Cabin

embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)

Tips:

* Empty floats - Double.Nan

RESOURCESTitanic: Machine Learning from Disaster

Special thanks!◦Matthias Brandewinder for the Machine Learning samples

◦ http://www.clear-lines.com/blog/

◦ Tomas Petricek & Gustavo Guerra for FSharp.Data library◦ http://fsharp.github.io/FSharp.Data/

◦ F# Team for Type Providers◦ http://blogs.msdn.com/b/dsyme/archive/2013/01/30/twelve-type-providers-in-pictures.aspx

◦Peter Harrington’s for the Machine Learning in Action code samples◦ http://www.manning.com/pharrington/

◦Kaggle for the Titanic data set◦ http://www.kaggle.com/c/titanic-gettingStarted

Machine Learning Job TrendsSource indeed.co.uk

What next?F# Machine Learning information

◦ http://fsharp.org/machine-learning/

Random Forests

◦http://tinyurl.com/randomforests

Progressive F# Tutorials

◦http://skillsmatter.com/event/scala/progressive-f-tutorials-2013