Exam 1

Question 1

Define Predictive Analytics

Answer 1

Technology that learns from experience (data) to predict the future behavior of individuals in order o drive better decisions. (John Elder prediction of the stock market)

Question 2

How does PA relate to Organization Learning

Answer 2

PA is the process by which an organization learns from experience it has collectively gained across its team members and computer systems. Optimizing operations by making every day routines more efficient. i.e. catch more fraud, avoid bad debtors, lure more online customers.

Question 3

Each Application of PA is defined by what?

Answer 3

What’s predicted, and what’s done about it

Question 4

The Prediction Effect

Answer 4

A little prediction goes a long way

Question 5

Predictive Model

Answer 5

A mechanism that predicts behavior of an individual, such as click, buy, lie, or die.

Question 6

How does Machine Learning relate to the Predictive Model

Answer 6

It takes characteristics of the individual as input and provides a predictive score as output. The higher the score, the more likely it is that the individual will exhibit the predicted behavior.

Question 7

How do training examples related to prediction

Answer 7

The predictive modeling process is a form of automated data crunching that learns from training examples, which must include both positive and negative examples. An organization needs to have positively identified in the pas some cases of what it would like to predict in the future. Positive and Negative examples that a predictive model learns from. What causes a behavior, and what doesn’t cause a behavior.

Question 8

5 things that relate to the ethics around policing data?

Answer 8

Retain, Access, Share, Merge, React; What can be stored and for how long can it be stored? How do you react to the data, do you use it for something discriminatory? The ethics of analytics all comes down to how you use it.

Question 9

Retain

Answer 9

What is stored and for how long

Question 10

Access

Answer 10

Which employees, types of personnel, or group members may retrieve and look at which data elements

Question 11

Share

Answer 11

What data may be disseminated to which parties within the organization, and to what external organizations

Question 12

Merge

Answer 12

What data may be joined together, aggregated, or connected.

Question 13

React

Answer 13

How may each data element be acted upon, determining an organization’s response, outreach, or other behavior.

Question 14

How is predictive analytics the opposite of privacy invasion

Answer 14

PA in and of itself does not invade privacy; its core process is the opposite of privacy invasion. PA doesn’t drill down to peer at any individual’s data. Instead, PA actually rolls up learning patterns that hold true in general by way of rote number crunching across the masses of customer needs. Data mining often appears to be a culprit when people misunderstand and completely revers its meaning. Privacy invasion would mean that you are drilling down to the individual person. It aggregates individual data to learn a pattern.

Question 15

is PA insight or Intrusion?

Answer 15

It depends on what the company does with the data (PA). Determine which employees might quit (or pregnant), and lay them off for this reason, would be bad.

Question 16

Is PA profiling? Why/Why not

Answer 16

Depending on how it is used. It is found that most terrorist are Muslim or Arabic, if you use this to discriminate against Muslims or Arabics, then this becomes profiling (specifically in policing).

Question 17

Why is transparency and Accountability so important in using PA for law enforcement?

Answer 17

Racism and demographics in relation to criminal background and crimes.

Question 18

What are the issues around the NSA and PA?

Answer 18

Both sides need to understand the other side better

Question 19

Open Data Movement

Answer 19

There’s a ton of data available and free. Easy to obtain data from the web.

Question 20

How does storage cost influence data availability

Answer 20

It has gone from very expensive, to basically 0. It is very flexible and elastic. You can get as much of it as you want…..why don’t you collect that data and keep it?

Question 21

Data Effect

Answer 21

No matter what, data is always predicted. May not be right, but always predicted. Data is always predictive.

Question 22

Predictive Variable

Answer 22

The variable that we measure

Question 23

Dependent Variable

Answer 23

The variable that is impacted by the predictor variable

Question 24

Frequency

Answer 24

The frequency that things occur (or don’t)

Question 25

Behavioral Predictors

Answer 25

What’s that individual done, how often, how many times a week, how many times a day, etc.

Question 26

Correlation vs Causation

Answer 26

Two things move in the same direction. example… Dow Jones increases when NFC wins the super bowl, this is just by chance. Just happened to occur, one doesn’t cause the other. To establish correlation you have to have a controlled experiment where you hold all things constant other than the variable you are manipulating

Question 27

Fooled by Randomness

Answer 27

Randomness, chance, luck. We create reasons for something happening, rather than attributing it to pure chance/luck/randomness. Tend to explain random outcomes as non-random

Question 28

Vast Search Pitfall

Answer 28

When you have huge amounts of data, many of the correlations are going to be false

Question 29

Machine Learning

Answer 29

Machine learning processes data to produce a predictive model. Data -> Machine Learning -> Predictive model.

Question 30

Micro Risk

Answer 30

Any one customer can be demoted to a micro risk. These micro risk can add up to large risks.
PA serves as an antidote to the poisonous accumulation of microrisks. It stands vigil, prospectively flagging each microrisk so the organization can do something about it (credit score). What an organization effectively learns with PA is how to decrease risk by way of anticipating microrisks. All organizations benefit from measuring and predicting the risk of bad behavior (defaults, cancellations, dropouts, accidents, fraud, crime) in this way PA transforms risk to opportunity.

Question 31

Micro Risk and how does PA help mitigate it?

Answer 31

Any one customer can be demoted to a micro risk. These micro risk can add up to large risks.
PA serves as an antidote to the poisonous accumulation of microrisks. It stands vigil, prospectively flagging each microrisk so the organization can do something about it (credit score). What an organization effectively learns with PA is how to decrease risk by way of anticipating microrisks. All organizations benefit from measuring and predicting the risk of bad behavior (defaults, cancellations, dropouts, accidents, fraud, crime) in this way PA transforms risk to opportunity.

Question 32

Predictor Variable

Answer 32

One factor about the individual.

Question 33

Univariate Model

Answer 33

Since it only considers one predictor variable, this is called a univariate model.

Question 34

Multivariate Model

Answer 34

Model considers multiple factors at once, instead of just one

Question 35

Decision Tree

Answer 35

Flowchart like tree structure, where each internal node denotes a test on an attribute. Each branch represents an outcome of the test. Each leaf holds a class label. Decision tree is the most powerful and popular tool for classification and prediction.

Question 36

How one builds a decision tree from the data

Answer 36

Find factors that best breaks down the low risk group even further, into two subgroups, then keep going. Do the same for the high risk group. Divide and conquer, and then divide some more. Don’t go too far. You start at the top and answer yes/no questions to arrive at a leaf. The leaf indicates the model’s predictive output for that individual.

Question 37

How do If-Then-Rules apply to decision tree logic

Answer 37

If yes, then go down left. If no, then go down right, etc.

Question 38

Lift

Answer 38

Sales in response to marketing. A single metric that compares the performance of predictive models, a kind of predictive multiplier. It tells you how many more target customers you can identify with a model than without one.

Question 39

Overlearning

Answer 39

Assuming too much. Mistaking noise for information, assuming too much about what has been shown within data. You’ve overlearned if you’ve read too much into the numbers, led astray from discovering the underlying truth.

Question 40

Induction

Answer 40

Reasoning from detailed facts to general principles. Is an art form.

Question 41

Deduction

Answer 41

Reasoning from the general to the particular (or from cause to effect). Much more straight forward, it’s applying known rules. “If all men are mortal and Socrates is a man, then deduction tells us Socrates is Mortal.”

Question 42

How training data helps validate PA models

Answer 42

It is used to test for overlearning. Hold aside some data to test the model. Randomly select a test set and quarantine it. Use the remaining portion of the data, the training set, to create the model. Then, evaluate the resulting model across the test set. Since the test set was not used to create the model, there’s no way the model would have captured its esoteric aspects, its eccentricities. However well the model does on the test set is a reasonable estimation of how well the model does in general. A true evaluation of its ability to predict.

Question 43

Induction Effect

Answer 43

Art drives machine learning; when followed by computer programs, strategies designed in part by informal human creativity succeed in developing predictive models that perform well on new cases. The fact that machine learning works tells us that we humans are smart enough - the hunches and intuitions that drive the design of methods to learn yet not overlearn pan out; i call this the induction effect. Figuring out what makes the best model.

Question 44

What’s gained by crowdsourcing PA?

Answer 44

PA crowdsourcing reaps the rewards brought by a diverse brainshare. The crowd almost always outperforms a single person.

Question 45

The Netflix Competition

Answer 45

Competition involving crowdsourcing. Challenged the world by requiring that the winner improve upon Netflix’s own established recommendation capabilities by 10%.

Question 46

Coopetition

Answer 46

Cooperative competition. Teams ended up working together and splitting the prize. What it was called in the Netflix competition.

Question 47

Why are Ensemble Models stronger?

Answer 47

It considers both models predictions on a case by case basis. Trained to predict which cases are weak points for each component model. Where there is a disagreement, teaming the models together provides the opportunity to improve performance.

Question 48

Ensemble Effect

Answer 48

When joined in an ensemble, predictive models compensate for one another’s limitations so the ensemble as a whole is more likely to predict correctly than its component models are. Robustness against overlearning. More models merged together, work better than when the models aren’t merged.

Question 49

Why is it difficult for AI to understand language?

Answer 49

Because the rules of language are fluent. Really hard to get a computer to understand the shades of meaning. Idioms are difficult to understand.

Question 50

How does PA relate to answering questions like Jeopardy?

Answer 50

Answering questions is not prediction, rather its models predicted the correctness of an answer. Given a predictive score to the correctness of the answer.

Question 51

How does Watson work (funnel down)?

Answer 51

Given a question, it takes three main steps: Collect thousands of candidate answers, for each answer amass evidence, apply predictive models to funnel down. After gathering thousands of candidate answers to a question, Watson funnels them down to spit out the single answer scored most highly by a predictive model.

Question 52

How Ensemble Models help Watson?

Answer 52

Watson incorporates ensembling in three ways: Combining evidence. Separate specialized models for specific question types. For each question, Watson iteratively applies several phases of predictive models, each of which can compensate for mistakes made by prior phases.

Question 53

Churn Modeling

Answer 53

Keeping customers by targeting the customers that are predicted to leave. Cheaper to convince a customer to stay than to acquire a new one.

Question 54

Response Modeling

Answer 54

Predicts the outcome for those we do contact, but not for those left uncontacted. Predicting which customers will purchase.

Question 55

Uplift Modeling

Answer 55

A predictive model that predicts the influence on an individual’s behavior that results from applying one treatment over another. How much more likely is this treatment to generate the desired outcome than the alternative treatment. Send a brochure or don’t? Figuring out the influence of a particular promotion on an individual. free phone or some other incentive. How much more likely is one treatment going to work over another one.

Question 56

Persuasion Effect

Answer 56

Although imperceptible, the persuasion of an individual can be predicted by uplift modeling, predictively modeling across two distinct training data sets that record, respectively, the outcomes of two competing treatments. Combine two paradigms: comparing treated and control results, and, predictive modeling. Being influenced is the thing that often happens to you that cannot be witnessed and that you can’t even be sure has happened afterward, but can be predicted in advance.

Question 57

Churn Uplift Modeling

Answer 57

Should we provide the customer a retention offer or not (active or passive treatment). Objective is obtain a positive impact of retention campaigns. Which customers can be persuaded to stay, and retention efforts should be targeted to these persuadable customers. Offers could be triggering some customers to leave who would have stayed otherwise. Comparing treated (contacted) and control (not contacted) results, and using predictive modeling. Determining what is the ROI for only contacting the ones that can be persuaded, and avoiding the DND’s and Sure Things. Predicting the customers that can be persuaded to stay, and only send offers out to these customers.