Data Preparation

1. Data Preparation

2. Accuracy Depends on the Data • What data is available for the task? • Is this data relevant? • Is additional relevant data available? • Who are the data experts? • How much data is available for the task? • How many instances? • How many attributes? • How many targets? • What is the quality of the data? • Noise • Missing values • Skew Guiding principle: Better to have a fair modeling method and good data, than to have the best modeling method and poor data

3. Data Types • Categorical/Symbolic • Nominal • No natural ordering • Ordinal • Ordered • Difference is well-defined (e.g., GPA, age) • Continuous • Ratio is well-defined (e.g., size, population) • Special cases • Time, Date, Addresses, Names, IDs, etc.

4. Type Conversion • Some tools can deal with nominal values internally, other methods (neural nets, regression, nearest neighbors) require/fare better with numeric inputs • Some methods require discrete values (most versions of Naïve Bayes) • Different encodings likely to produce different results • Only show some here as illustration

5. Categorical Data • Binary to numeric • E.g., {M, F}  {1, 2} • Ordinal to Boolean • From n values to n-1 variables • E.g., • Ordinal to numeric • Key: must preserve natural ordering • E.g., {A, A-, B+, B, …}  {4.0, 3.7, 3.4, 3.0, …} 

6. Continuous Data • Equal-width discretization • Equal-height discretization Skewed data leads to clumping • More intuitive breakpoints • Don’t split frequent values • Separate bins for special values Note: can also do class dependent if applicable

7. Other Useful Transformations • Standardization • Transforms values into the number of standard deviations from the mean • New value = (current value - average) / standard deviation • Normalization • Causes all values to fall within a certain range • Typically: new value = (current value - min value) / range • Neither one affects ordering!

8. Missing Data • Different semantics: • Unknown vs. unrecorded vs. irrelevant • E.g., iClicker selection: student not in class (unk), iClicker malfunction (unr), no preference (irr) • Different origins: • Measurement not possible (e.g., malfunction) • Measurement not applicable (e.g., pregnancy) • Collation of disparate data sources • Change in experimental design/data collection

9. Missing Data Handling • Remove records with missing values • Treat as separate value • Treat as don’t know • Treat as don’t care • Use imputation technique • Mode, median, average • Use regression • Danger: BIAS!

10. Outliers • Outliers are values that are thought to be out of range (e.g., body temp. = 115F) • Approaches: • Do nothing • Enforce upper and lower bounds • Use discretization • Problem: • Error vs. exception • E.g., a 137 year-old lady is an error, an ostrich that does not fly is an exception

11. Useless Attributes • Attributes with no or little variability • Rule of thumb: remove a field where almost all values are the same (e.g., null), except possibly in minp% or less of all records • Attributes with maximum variability • Rule of thumb: remove a field where almost all values are different for each instance (e.g., id/key)

12. Dangerous Attributes • Highly correlated with another feature • In this case the attribute may be redundant and only one is needed • Highly correlated with the target • Check this case as the attribute may just be a synonym with the target (data leak) and will thus lead to overfitting (e.g., the output target was bundled with another product so they always occur together)

13. Class Skew • When occurrences of 1+ output classes are rare • Learner might just learn to predict the majority class • Approaches: • Undersampling: keep all minority instances, and sample majority class to reach desired distribution (e.g., 50/50) – may lose data • Oversampling: keep all majority instances, and duplicate minority instances to reach desired distribution – may cause overfit • Use ensemble technique (e.g., boosting) • Use asymmetric misclassification costs (P/R)

14. Attribute Creation • Transform existing attributes • E.g., use area code rather than full phone number, determine vehicle make from VIN • Create new attributes • E.g., compute BMI from weight and height, derive household income from spouses’ salaries, extract frequency or mean-time to failure from event dates • Requires creativity and often domain knowledge, but can be very effective in improving learning

15. Dimensionality Reduction • At times the problem is not lack of attributes but the opposite, overabundance of attributes • Approaches: • Attribute selection • Considers only a subset of available attributes • Requires a selection mechanism • Attribute transformation (aka, feature extraction) • Creates new attributes from existing ones • Requires some combination mechanism

16. Attribute Selection • Simple approach: • Select top N fields using 1-field predictive accuracy (e.g., using Decision Stump) • Ignores interactions among features • Better approaches: • Wrapper-based • Uses learning algorithm and accuracy as goodness-of-fit • Filter-based • Uses merit metric as goodness-of-fit, Independent of learners

17. Wrapper-based Attribute Selection • Split dataset into training and test sets • Using training set only: • BestF = {} and MaxAcc = 0 • While accuracy improves / stopping condition not met • Fsub = subset of features [often best-first search] • Project training set onto Fsub • CurAcc = cross-validation estimate of accuracy of learner on projected training set • If CurAcc > MaxAcc then BestF = Fsub • Project both training and test sets onto BestF

18. Filter-based Attribute Selection • Split dataset into training and test sets • Using training set only: • BestF = {} and MaxMerit = 0 • While merit improves / stopping condition not met • Fsub = subset of features [often best-first search] • CurMerit = heuristic value of merit of Fsub • If CurMerit > MaxMerit then BestF = Fsub • Project both training and test sets onto BestF

19. Attribute Transformation: PCA • Principal components analysis (PCA) is a linear transformation that chooses a new coordinate system for the data set such that: • The greatest variance by any projection of the data set comes to lie on the first axis (then called the first principal component) • The second greatest variance on the second axis • Etc. • PCA can be used for reducing dimensionality by eliminating the later principal components

20. Overview of PCA • The algorithm works as follows: • Compute covariance matrix and corresponding eigenvalues and eigenvectors • Order eigenvalues from largest to smallest • Eigenvectors with the largest eigenvalues correspond to dimensions with the strongest correlation in the dataset • Select a number of dimensions (N) • Ratio of sum of selected top N eigenvalues to sum of all eigenvalues is amount of variance explained by corresponding N eigenvectors [could also pick variance threshold] • The N principal components form the new attributes

21. PCA – Illustration (1) • Eigenvectors are plotted as dotted lines (perpendicular) • First eigenvector goes through the “middle” of the points, like line of best fit • Second eigenvector gives other, less important, dimension in the data • Points tend to follow first line, off by small amount

22. PCA – Illustration (2) • Variation along the principal component is preserved • Variation along the other component has been lost

23. Bias in Data • Selection/sampling bias • E.g., collect data from BYU students on college drinking • Sponsor’s bias • E.g., PLoS Medicine article: 111 studies of soft drinks, juice, and milk that cited funding sources (22% all industry, 47% no industry, 32% mixed). Proportion with unfavorable [to industry] conclusions was 0% for all industry funding versus 37% for no industry funding • Publication bias • E.g., positive results more likely to be published • Data transformation bias

24. Impact of Bias on Learning • If there is bias in the data collection or handling processes, then: • You are likely to learn the bias • Conclusions become useless/tainted • If there is no bias, then: • What you learn will be “valid”

25. Take Home Message • Be thorough • Ensure you have sufficient, relevant, quality data before you go further • Consider potential data transformation • Uncover existing data biases and do your best to remove them (do not add new sources of data bias, maliciously or inadvertently)

26. Twyman’s Law

27. Cool Findings • 5% of our customers were born in the same day (including year) • There is a sales decline on April 2nd, 2006 on all US e-commerce sites • Customers willing to receive emails are also heavy spenders

28. What Is Happening? • 11/11/11 is the easiest way to satisfy the mandatory birth date field! • Due to daylight saving starting, the hour from 1AM to 2AM does not exist and hence nothing will be sold during that period! • The default value at registration time is “Accept Emails”!

29. Take Home Message • Cautious optimism • Twyman’s Law: Any statistic that appears interesting is almost certainly a mistake • Many “amazing” discoveries are the result of some (not always readily apparent) business process • Validate all discoveries in different ways

30. Simpson’s Paradox

31. “Weird”Findings • Kidney stone treatment: overall treatment B is better; when split by stone size (large/small), treatment A is better • Gender bias at UC Berkeley: overall, a higher percentage of males than females are accepted; when split by departments, the situation is reversed • Purchase channel: overall, multi-channel customers spend more than single-channel customers; when split by number of purchases per customer, the opposite is true • Presidential election: overall, candidate X’s tally of individual votes is highest; when split by states, candidate Y wins the election

32. What Is Happening? • Kidney stone treatment: neither treatment worked well against large stone, but treatment A was heavily tested on those • Gender bias at UC Berkeley: departments differed in their acceptance rates and female students applied more to departments were such rates were lower • Purchase channel: customers that visited often spent more on average and multi-channel customers visited more • Presidential election: winner-take-all favors large states

33. Take Home Message • These effects are due to confounding variables • Combining segments weighted average • if it is possible that • Lack of awareness of the phenomenon may lead to mistaken/misleading conclusions • Be careful not to infer causality from what are only correlations • Only sure cure/gold standard (for causality inference): controlled experiments • Careful with randomization • Not always desirable/possible (e.g., parachutes) • Confounding variables may not be among the ones we are collecting (latent/hidden) • Be on the look out for them!

34. Intro to Weka