It s a Machine World. Predictive Analytics with Machine Learning

Transcription

1 It s a Machine World Predictive Analytics with Machine Learning Greg Deckler

2 It s a Machine World Predictive Analytics with Machine Learning Greg Deckler

3 Greg Deckler Fusion Alliance Solution Director Cloud Services Columbus, OH United States LinkedIn: 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

4 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?

5 About Fusion Alliance

6 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.

7 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

8 History Dartmouth Summer Research Project on Artificial Intelligence John McCarthy, Marvin Minsky, Nathan Rochester, Claude Shannon Arthur Samuel, Allen Newell, Herbert Simon, Dr. Heintz Doofenshmirtz, Alloyse von Roddenstein, Dr. Diminutive, Dr. Killbot, Dr. Goatfish

9 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

10 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

11 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

12 Why Machine Learning? Exponential data growth Cheap global digital storage Ubiquitous computing power The rise of big data analytics

13 Examples of Predictive Analytics Spam/junk 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 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

14 Core Concepts Data Preparation Types of Learning Approaches Outputs Questions Linearity Algorithms Training, Scoring and Evaluation

15 Data Preparation Relevant Connected Accurate Enough Access

16 Data Preparation - Relevant

17 Data Preparation - Connected

18 Data Preparation - Accurate

19 Data Preparation - Enough

20 Learning Supervised Unsupervised Reinforcement

21 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

22 Approaches Supervised learning AODE Artificial neural network Backpropagation Autoencoders Hopfield networks Boltzmann machines Restricted Boltzmann Machines Spiking neural networks Bayesian statistics Bayesian network Bayesian knowledge base Case-based reasoning Gaussian process regression Gene expression programming Group method of data handling Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum message length Nearest Neighbor Algorithm Analogical modeling Probably approximately correct learning Ripple down rules Symbolic machine learning Support vector machines Random Forests Ensembles of classifiers Bootstrap aggregating (bagging) Boosting (meta-algorithm) Ordinal classification Information fuzzy networks (IFN) Conditional Random Field ANOVA Hidden Markov models Linear classifiers Fisher's linear discriminant Linear regression Logistic regression Multinomial logistic regression Naive Bayes classifier Perceptron Support vector machines Quadratic classifiers k-nearest neighbor Boosting Decision trees C4.5 Random forests ID3 CART SLIQ SPRINT Bayesian networks Naive Bayes

23 Approaches Reinforcement learning Temporal difference learning Q-learning Learning Automata SARSA Semi-supervised learning Generative models Low-density separation Graph-based methods Co-training Deep learning Deep belief networks Deep Boltzmann machines Deep Convolutional neural networks Deep Recurrent neural networks Hierarchical temporal memory Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Artificial neural network Self-organizing map Association rule learning Apriori algorithm Eclat algorithm FP-growth algorithm Hierarchical clustering Single-linkage clustering Conceptual clustering Cluster analysis K-means algorithm Fuzzy clustering DBSCAN OPTICS algorithm Outlier Detection Local Outlier Factor

24 Outputs Classification Anomaly Detection Regression Clustering Density Estimation Dimensionality Reduction

25 Questions Is this A or B? Is this weird? How much, how many? How is this organized? What should I do next?

26 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?

27 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?

28 What Questions does ML Answer? What will the temperature be next Tuesday? What will my fourth quarter sales be?

29 What Questions does ML Answer? Which viewers like the same types of movies? Which printer models fail the same way?

30 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?

31 Linearity

32 Algorithms Classification Anomaly Detection Regression Clustering

33 Algorithms Logistic Regression Support Vector Machine

34 Algorithms One-vs-All Multiclass Classifier Decision Trees

35 Algorithms Linear Regression Neural Networks

36 Algorithms K-means Principal Components Analysis

37 k-nearest Neighbors It s Just Simple Math... AODE Support Vector Machines Boltzmann Machines Random Forests k-means Clustering Fischer s Linear Discriminant Naive Bayes Classifier Quadratic Classifiers Perceptron

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39 Training, Scoring and Evaluation Training Scoring Evaluation Cross Validation Confusion Matrix Accuracy (ACC) Precision (PPV) Recall (TPR) F1 Score (F 1 ) Area Under Curve

40 Break

41 Break

42 Putting Theory into Practice Brainstorming What questions could we answer with data? Ranking What questions are most suitable for machine learning?

43 Putting Theory into Practice Value Suitability Data Available Complexity Score Question Type 1-5 Low 1-5 Low to High 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" Is this A or B? Classification Route deliveries to ensure guaranteed timeframe is achieved What should I do now? Reinforcement Price products for greater unit sales and profitability How much, how many? Regression Analyze social media to understand customer personas How is this organized? Clustering Forecast sales and labor staffing efficiently How much, how many? Regression Determine media for best return on marketing investments How much, how many? How is this organized? Regression Clustering

44 DEMO Don t Panic

45 Common Issues Bias Class Imbalance Problem

46 Bias Bias in the data Bias created by using ML

47 Class Imbalance Problem If there is a dataset consisting of 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 genuine transactions as fraudulent transactions. Model 2 classified 2 out of 10 fraudulent transactions as genuine transactions and 100 out of genuine transactions as fraudulent transactions.

48 Class Imbalance Problem

49 Class Imbalance Problem

50 Class Imbalance Problem - Solutions Cost function based approaches Sampling

51 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).

52 Sampling - Undersampling

53 Sampling - Oversampling

54 Sampling - SMOTE

55 Operationalizing Machine Learning 1. Extract Power BI 4. Publish Power BI Service 2. Call web service R Gateway 3. Return predictions 5. Schedule refresh Azure ML

56 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

57 Questions? Try Machine Learning, what s the worst that could happen?

58 Questions? Try Machine Learning, what s the worst that could happen?