Business Cases for Machine Learning

Transcription

1 Business Cases for Machine Learning Matching Predictive Algorithms to Usage

2 Overview Outline basic problem Predictive modeling templates Discussion of common business problems What algorithms fit?

3 Factors to Consider Business problem to be solved What shapes can problem be hammered into Size and Speed Requirements Training Real time evaluation Prediction performance requirements Shape and features of predictive models

4 Predictive Algorithm Classes Fitting a scalar function Linear and non-linear Recommender Association Rules Clustering Text Clustering (other text mining e.g. NER) Others (neural nets, belief nets, graphical models, etc.)

5 Fitting a Scalar Function UCI Abalone Data Attribute Sex Length Diameter Height Whole weight Shucked weight Viscera weight Shell weight Label (age) Type nominal continuous continuous continuous continuous continuous continuous continuous integer

6 Algo for Fitting Scalar Function Linear OLS, Lasso, Lars, ElasticNet SVM Linear Discriminant Analysis Non-Linear Basis expansion with linear method SVM Ensemble methods k-nearest neighbors

7 Recommender Given matrix of ratings (perhaps binary) i1 i2 i3 i4 i5 u ?? u2? 3 2 4? u3??? 2 2 u4 2 3?? 2 Fill in the?

8 Recommender Algorithms Collaborative Filtering SVD rank reduction Rank reduction by penalized gradient descent

9 Association Rules -Given cash register tapes: {bread, butter, beer} {milk, cookies, peanut butter, apricots} {apricots, broccoli, fair trade coffee, bagels} Figure out what items go together.

10 Association Rule Algorithms Form associations of the form Item1 is strongly associated with item2 if: Many of the transactions that have item2 also have item1 An exercise in counting but an enormous one A-priori rule used to make counting efficient Special case: auto-complete where order matters. (CEP in finance)

11 Clustering Algorithms You re given this You want this Why would you want artificial labels?

12 Clustering Algorithms K means Mixture Models

13 Text Clustering Doc\word Four Score And Seven Years Ago our G-addy Using word count structures text: - Documents are points in vector space - Ordinary clustering - Mixture Model

14 Text Clustering Algorithms Usual vector-space clustering Sparsity -> better results with svd dimension reduction Mixture models (LDA, topic modeling)

15 Often start with unstructured Structuring problems entails choices Structuring text requires choices about stop words, aggressive stemming, proper names, etc. Counting ad clicks from log files requires choices about time span between ad display and click May require iteration

16 Some examples Amazon ebay Yelp NYTimes

17 Amazon Visitor comes to Amazon What links does Amazon show?

18 ebay User types in iphone as search query What links do they show? What s their calculation?

19 Yelp Yelp user searches for coit tower How do they calculate also viewed links?

20 NYTimes NYTimes sends s to subscribers How do they decide what articles to include?

21 References Here are a couple of the references I mentioned during the talk Algo Comparison - Algo Comparison in high dimension - If any of you have questions, problems or startup ideas to bat around feel free to send me an .