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It’s a Machine World
Predictive Analytics with Machine Learning
Greg Deckler
@GregDeckler
It’s a Machine World
Predictive Analytics with Machine Learning
Greg Deckler
@GregDeckler
Greg Deckler
Fusion AllianceSolution Director – Cloud ServicesColumbus, OH United States• Email: [email protected]• LinkedIn: https://www.linkedin.com/in/gregdeckler• Twitter: @GregDeckler• PBI Community: smoupre• ScoopIt: Business Intelligence Insights
• Founder of the Columbus Azure ML and Power BI User Group• Author of Achieving Process Profitability, Building the IT Profit Center
Agenda• What is Machine Learning?
• History of Machine Learning
• Why Machine Learning?
• Examples of Predictive Analytics
• Core Concepts
• Putting Theory into Practice
• Demo
• Common Issues in ML
• Operationalizing ML
• Resources
• Questions?
What is Machine Learning?• Machine learning can be described as computing systems that
improve with experience. It can also be described as a method of turning data into software. Whatever term is used, the results remain the same; data scientists have successfully developed methods of creating software “models” that are trained from huge volumes of data and then used to predict certain patterns, trends, and outcomes.
• Predictive analytics is the underlying technology behind Machine Learning, and it can be simply defined as a way to scientifically use the past to predict the future to help drive desired outcomes.
History
• Machine learning was born from the quest for artificial intelligence
• Antiquity has stories of artificial beings
• The study of form or mechanical reasoning began with ancient philosophers
• But, where things really got moving was right around 1956…
History - 1956
• Dartmouth Summer Research Project on Artificial Intelligence• John McCarthy, Marvin Minsky,
Nathan Rochester, Claude Shannon• Arthur Samuel, Allen Newell,
Herbert Simon, Dr. HeintzDoofenshmirtz, Alloyse von Roddenstein, Dr. Diminutive, Dr. Killbot, Dr. Goatfish
History – Arthur Lee Samuel•Coined the term “machine
learning” in 1959
• The 1955 version of his checker playing program, the Samuel Checkers-playing Program, is arguably the first example of a self-learning program
History –The Rift
• By 1980, machine learning as well as neural networks were out-of-favor within AI in favor of expert systems
• Machine learning, reorganized as a separate field, started to flourish in the 1990s.
• Changed goal to solvable problems of a practical nature
• Shifted away from symbolic approaches to statistics and probability theory
Machine Learning Recap• Evolved from pattern recognition and
computational learning theory
• Explores the study and construction of algorithms that can learn and make predictions on data
• Closely related and overlaps with computational statistics
• Has strong ties to mathematical optimization
• Sometimes conflated with data mining
• Used within the field of data analytics
• Tom M. Mitchell’s formal definition (1997): "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."
• In short, turn data into programs to predict something
Why Machine Learning?
•Exponential data growth
•Cheap global digital storage
•Ubiquitous computing power
•The rise of big data analytics
Examples of Predictive Analytics
• Warranty reserve estimation
• Propensity to buy
• Demand forecasting
• Predictive inventory planning
• Recommendation engines
• Dynamic pricing
• Credit worthiness evaluation
• Smart grid management
• Energy supply and demand
• Carbon emissions and trading
• Patient triage optimization
• Spam/junk email filters
• Mortgage applications
• Various forms of pattern recognition
• Life insurance
• Medical insurance
• Liability/property insurance
• Credit card fraud detection
• Airline flight scheduling
• Web search page results
• Predictive maintenance
• Proactive health management
Core Concepts•Data Preparation
•Types of Learning
•Approaches
•Outputs
•Questions
•Linearity
•Algorithms
•Training, Scoring and Evaluation
Approaches
• Decision tree learning
• Association rule learning
• Artificial neural networks
• Deep learning
• Inductive logic programming
• Support vector machines
• Clustering
• Bayesian networks
• Reinforcement learning
• Representation learning
• Similarity and metric learning
• Sparse dictionary learning
• Genetic algorithms
• Rule-based machine learning
• Learning classifier systems
ApproachesSupervised learningAODE
Artificial neural network BackpropagationAutoencodersHopfield networksBoltzmann machinesRestricted Boltzmann MachinesSpiking neural networks
Bayesian statisticsBayesian networkBayesian knowledge base
Case-based reasoning
Gaussian process regression
Gene expression programming
Group method of data handling
Inductive logic programming
Instance-based learning
Lazy learningLearning AutomataLearning Vector QuantizationLogistic Model Tree
Minimum message lengthNearest Neighbor AlgorithmAnalogical modeling
Probably approximately correct learning Ripple down rulesSymbolic machine learningSupport vector machinesRandom Forests
Ensembles of classifiersBootstrap aggregating (bagging)Boosting (meta-algorithm)
Ordinal classificationInformation fuzzy networks (IFN)Conditional Random FieldANOVAHidden Markov models
Linear classifiersFisher's linear discriminantLinear regressionLogistic regressionMultinomial logistic regressionNaive Bayes classifierPerceptronSupport vector machines
Quadratic classifiersk-nearest neighborBoosting
Decision treesC4.5Random forestsID3CARTSLIQSPRINT
Bayesian networksNaive Bayes
Approaches Unsupervised learningExpectation-maximization algorithmVector QuantizationGenerative topographic mapInformation bottleneck methodArtificial neural network
Self-organizing mapAssociation rule learning
Apriori algorithmEclat algorithmFP-growth algorithm
Hierarchical clusteringSingle-linkage clusteringConceptual clustering
Cluster analysisK-means algorithmFuzzy clusteringDBSCANOPTICS algorithm
Outlier DetectionLocal Outlier Factor
Semi-supervised learningGenerative modelsLow-density separationGraph-based methodsCo-training
Deep learningDeep belief networksDeep Boltzmann machinesDeep Convolutional neural networksDeep Recurrent neural networksHierarchical temporal memory
Reinforcement learningTemporal difference learningQ-learningLearning AutomataSARSA
Outputs
•Classification
•Anomaly Detection
•Regression
•Clustering
•Density Estimation
•Dimensionality Reduction
Questions
•Is this A or B?•Is this weird?•How much, how many?•How is this organized?•What should I do next?
What Questions does ML Answer?
•Will this tire fail in the next 1,000 miles: Yes or no?•Which brings in more customers: a $5 coupon or a 25% discount?
What Questions does ML Answer?
•If you have a car with pressure gauges, you might want to know: Is this pressure gauge reading normal?•If you're monitoring the internet you’d want to know: Is this message from the internet typical?
What Questions does ML Answer?
•What will the temperature be next Tuesday?•What will my fourth quarter sales be?
What Questions does ML Answer?
•Which viewers like the same types of movies?•Which printer models fail the same way?
What Questions does ML Answer?
•If I'm a temperature control system for a house: Adjust the temperature or leave it where it is?•If I'm a self-driving car: At a yellow light, brake or accelerate?•For a robot vacuum: Keep vacuuming, or go back to the charging station?
It’s Just Simple Math...
AODE
Naive Bayes ClassifierFischer’s Linear Discriminant
Boltzmann Machines
Perceptron
Random Forests
k-Nearest Neighbors
Quadratic Classifiers
k-Means Clustering
Support Vector Machines
Training, Scoring and Evaluation• Training• Scoring• Evaluation• Cross Validation• Confusion Matrix• Accuracy (ACC)• Precision (PPV)• Recall (TPR)• F1 Score (F1)• Area Under Curve
Putting Theory into Practice
•Brainstorming – What questions could we answer with data?
•Ranking – What questions are most suitable for machine learning?
Putting Theory into PracticeValue Suitability Data Available Complexity Score Question Type1-5 Low to High
1-5 Low to High
1-5 Low to High 1-5 High to Low
1-5 Bad to Good
Predict when and why a customer becomes a "Leaver" 3 5 5 4 4.25Is this A or B? Classification
Route deliveries to ensure guaranteed timeframe is achieved 3 2 1 1 1.75What should I do now? Reinforcement
Price products for greater unit sales and profitability 3 2 2 2 2.25How much, how many? RegressionAnalyze social media to understand customer personas 3 3 3 2 2.75How is this organized? Clustering
Forecast sales and labor staffing efficiently 3 4 3 3 3.25How much, how many? RegressionDetermine media for best return on marketing investments 3 3 4 5 3.75
How much, how many?How is this organized?
RegressionClustering
Class Imbalance Problem
• If there is a dataset consisting of 10000 genuine and 10 fraudulent transactions, the classifier will tend to classify fraudulent transactions as genuine transactions. The reason can be easily explained by the numbers. Suppose the machine learning algorithm has two possible outputs as follows:
• Model 1 classified 7 out of 10 fraudulent transactions as genuine transactions and 10 out of 10000 genuine transactions as fraudulent transactions.
• Model 2 classified 2 out of 10 fraudulent transactions as genuine transactions and 100 out of 10000 genuine transactions as fraudulent transactions.
Cost Function Based Approaches
The intuition behind cost function based approaches is that if we think one false negative is worse than one false positive, we will count that one false negative as, e.g., 100 false negatives instead. For example, if 1 false negative is as costly as 100 false positives, then the machine learning algorithm will try to make fewer false negatives compared to false positives (since it is cheaper).
Operationalizing Machine Learning
R Gateway
Power BIPower BI Service
Azure ML
1. Extract 4. Publish
2. Call web service
3. Return predictions
5. Schedule refresh
Platforms
• Automatic Business Modeler
• Algorithmia• algorithms.io• Amazon Machine Learning• BigML• DataRobot• FICO Analytic Cloud
• Google Prediction API• HPE Haven OnDemand• IBM’s Watson Analytics• Microsoft Azure Machine
Learning• MLJAR.com• PurePredictive• Yottamine
Questions?
•Try Machine Learning, what’s the worst that could happen…?•[email protected]•@GregDeckler
Questions?
•Try Machine Learning, what’s the worst that could happen…?•[email protected]•@GregDeckler