2017 Predictive Analytics Symposium

Session 13, Getting Started: Sources of Tools and Training

Moderator: Min Mercer, FSA, MAAA

Presenters:

Mary Pat Campbell, FSA, MAAA Michael Cletus Niemerg, FSA, MAAA

SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer

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Getting Started: Sources of Tools and TrainingResources for Predictive Analytics in R

Mary Pat Campbell, FSA, MAAA

September 2017

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PREDICTIVE ANALYTICS IN RBeginner Resources

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INTRO TO STATISTICAL LEARNINGOnline course, textbook, and R exercises

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Online Course – Hands On, Simple Examples

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Class link: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/

Stanford Online Lagunita: Statistical Learning – Self-Paced Course

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Multimedia Approach, Convenient to Try Offline

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1.1 Opening Remarks

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Quick Quizzes to Test Understanding

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Chapter 7 Quiz: Moving Beyond Linearity

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Instant Feedback and Explanation

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Chapter 7 Quiz: Moving Beyond Linearity

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Some Questions are Easier than Others...

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Chapter 4.1: Introduction to Classification Problems

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Textbook: An Introduction to Statistical Learning with Applications in R

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Book Page:

http://www-bcf.usc.edu/~gareth/ISL/book.html

Fourth Printing:

http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf

Simplified mathematical/statistical underpinning More rigorous approach: The Elements of Statistical Learning

Covers same material as in the course

Exercises: Important to Try BothConceptual Applied

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Conceptual Exercises: Curse of Dimensionality

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ISLR, Chapter 4, Conceptual Exercises -- #4 – the problem with local approaches

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Conceptual Exercises: Curse of Dimensionality

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ISLR, Chapter 4, Conceptual Exercises -- #4 – the problem with local approaches

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Conceptual Exercises: Curse of Dimensionality

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ISLR, Chapter 4, Conceptual Exercises -- #4 – the problem with local approaches

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Conceptual Exercises: Backward Solving for Sample

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Curse of Dimensionality: The Ever-Expanding Cube

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Wolfram Alpha query: https://www.wolframalpha.com/input/?i=lim(10%5E(-1%2Fd),+d+-%3E+infinity)&rawformassumption=%7B%22C%22,+%22d%22%7D+-%3E+%7B%22Variable%22%7D&rawformassumption=%22UnitClash%22+-%3E+%7B%22d%22,+%7B%22Days%22%7D%7D

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KAGGLE KERNELSOnline playground

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Kaggle Kernels: Play With Other People’s Code!

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Random Forest And Nearest Neighbors on a Few Blockshttps://www.kaggle.com/apapiu/random-forest-on-a-few-blocks

Alexandru Papiu, https://www.kaggle.com/apapiu/random-forest-on-a-few-blocks Accessed 11 Sept 2017

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Can See Code and Its Result: Graph Example

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ggplot(small_train, aes(x, y )) +

geom_point(aes(color = place_id)) +

theme_minimal() +

theme(legend.position = "none") +

ggtitle("Check-ins colored by place_id")

Alexandru Papiu, https://www.kaggle.com/apapiu/random-forest-on-a-few-blocks Accessed 11 Sept 2017ggplot cheatsheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

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Can See Code and Its Result: Non-Graph Example

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Code:model_knn = FNN::knn(train = X, test = X_val, cl = small_train$place_id, k = 15) preds <- as.character(model_knn) truth <- as.character(small_val$place_id) mean(truth == preds)

Result:## [1] 0.5151964

Code:set.seed(131L) small_train$place_id <- as.factor(small_train$place_id) # ranger needs factors for classificationmodel_rf <- ranger(place_id ~ x + y + accuracy + hour + weekday + month + year,

small_train, num.trees = 100, write.forest = TRUE, importance = "impurity")

Result:## Growing trees.. Progress: 36%. Estimated remaining time: 55 seconds. ## Growing trees.. Progress: 86%. Estimated remaining time: 10 seconds.Alexandru Papiu, https://www.kaggle.com/apapiu/random-forest-on-a-few-blocks Accessed 11 Sept 2017

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Other Kaggle Kernels to Try in R

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Exploring the Titanic Datasethttps://www.kaggle.com/mrisdal/exploring-survival-on-the-titanicGreat data set for beginners – passenger list on Titanic... and who survived Yes/no classification problemBeginners competition: https://www.kaggle.com/c/titanic

Exploratory Analysis Zillowhttps://www.kaggle.com/philippsp/exploratory-analysis-zillowshows correlation plots

Wiki Traffic Forecast Explorationhttps://www.kaggle.com/headsortails/wiki-traffic-forecast-exploration-wtf-edahas hidden code – unhide to see

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Titanic Survival Data Set

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SOURCE: Megan Risdal, https://www.kaggle.com/mrisdal/exploring-survival-on-the-titanic

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Fork that Script!

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See How It’s Done

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SOURCE: Philipp Spachtholz, https://www.kaggle.com/philippsp/exploratory-analysis-zillow Accessed 12 Sept 2017

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See How It’s Done

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SOURCE: Philipp Spachtholz, https://www.kaggle.com/philippsp/exploratory-analysis-zillow Accessed 12 Sept 2017

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GOT YOUR OWN RECOMMENDATIONS?

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Getting Started: Sources of tools and trainingSession 13September 2017 – Predictive Analytics Symposium

Michael Niemerg, FSA, MAAAHealthcare Actuary, Milliman Inc.

Machine Learning Toolbox

Statistics and Algorithms

Programming

Data wrangling

Data Visualization and Communication

Domain Knowledge

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So many languages…

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So many algorithms…

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www.machinelearningmastery.com

What’s Common in the Insurance Industry

Languages R

Python

SAS

Algorithms Generalized Linear Models

Penalized Regression

Decision Trees

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What I wished I had known…

Don’t be intimidated Theory is deep – application is shallow

You don’t need an advanced math/stats/CS degree

Just get started Start with a single programming language and a problem

Don’t focus on only algorithms or only programming – do both

Spend your time wisely Focus on what matters to you

Get the gist of what isn’t important to you

Know your goals Are you curious to see what the buzz is about?

Do you need help with a specific problem?

Do you want to move into a predictive modeling career?

Just get started already!

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Books

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Online Education

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www.coursera.orgwww.datacamp.com

www.kaggle.com

www.udacity.com

www.machinelearningmastery.comwww.udemy.com

Useful Websites

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https://dataelixir.com/

www.r-bloggers.com/

www.statsblogs.com/

www.win-vector.com/blog

www.datatau.com

www.kdnuggets.com

www.datatau.com

Personal Favorites

Book: An Introduction to Statistical Learning

Online Course: Machine Learning (Coursera – Andrew Ng)

Website: www.datatau.com

Blog/Newsletter: Data Elixir

Dataset Repository: Kaggle

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Machine Learning in Action

Advantages Gentle on the math

Good introduction to the core concepts

First principles code and examples

Disadvantages Some familiarity with Python

Doesn’t teach pragmatic programming

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Gradient Descent

A predictive model can have many possible parameter valuesSome parameters are better than others – how do we find the best values?

AlgorithmDetermine Learning Rate Initialize variable weightsRepeat R times: Calculate the gradient Update the weights:

Weights = Weights + Learning Rate * Gradient

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Gradient Descent – Example

Goal – Using a vector of input data, find a single predicted value ( �𝑦𝑦) that minimizes our error function when compared to the dataData – [ 107, 93, 105, 107, 95, 82, 110, 104, 99, 99]Error Function:

�12𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐴𝐴𝐴𝐴𝑃𝑃𝑃𝑃 2

Gradient:−� 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐴𝐴𝐴𝐴𝑃𝑃𝑃𝑃

Parameters: Start with an initial guess of 165 Perform 5 rounds of gradient descent Use a learning rate of .05

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Gradient Descent – Example

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Logistic Regression w/ Gradient Descent

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Y X1 X20 1.75 1.370 1.05 1.520 2.43 1.310 2.06 0.51 1.45 2.641 0.95 4.381 1.97 3.51 1.51 3.69

Category 0 = WhiteCategory 1 = Black

Sample of Data Scatterplot of Full Dataset

Let’s start with a randomly generated dataset…

Logistic Regression

�𝑌𝑌 =1

1 + 𝑃𝑃−(𝛽𝛽0+𝛽𝛽1𝑥𝑥1+𝛽𝛽2𝑥𝑥2)

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Logistic Regression Decision Boundary

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Thank you