Machine learning it is time...

Machine learning it is time...

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  Machine Learning Sandip Chatterjee
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  Machine learning overviewand some applications Why should I care and why now? Closer look at the inner workings and some algorithms How do I get started? Did somebody say testing? Is it important? Give me some more pointers..... What is covered?
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  MEANS TO ANEND 3
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  Human • Innate abilityto think, ask questions, what questions needs to be answered, distinguish right from wrong (which we call it as the Expert Opinion) • Usually limited to work with ~ 4 variables and their interactions when trying to solve a problem/accomplish a task - 4 variables can be equated to 4 dimensions Human vs Machines – How different are we? 4 Machines • Good at answering questions, look for patterns in data that may not be possible by human • Compared to humans machines can be trained to look at N features/variables • Machines can learn/remember patters to be applied to future use cases • Never needs a break…
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   NLP –Siri, Google, Cortana  Marketing – Targeting your at Risk customer using Uplift Models  Entertainment – Netflix, IMDB movie recommendations  Health Care - Gene Sequencing, Cancer detection, Hospital Re- admissions  Politics - Election Outcomes – Nate Silver – 538 Blog  Auto Industry - Self driving cars, Auto Pilot  Insurance – Premium, Reserve, Intervention, Fraud, Subro  Productivity / Office – Email Spam vs Ham  Manufacturing - Predict Machine Failures  Other – Predicting Wait times and yes an all time favorite “Weather Forecasting…” What are some examples of Machine Learning? 5
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  To understand howthis works, we can refer back to how a child learns…aka how we learn… - Children picture book – To learn about a dog, the child has to learn about the Shape, Size, Color, Key Characteristics, Variations - Machine learns similar to a child / human by analyzing the data set, learning/identifying patterns in the data, learning/identifying variations/generalizations - The process looks like… Ok… What is Machine Learning? 6 Data InferencesConcepts Data Storage Abstraction Generalization Evaluation
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  The next stepin this evolution is to combine the power of the two [Human and Machines] to push the boundaries of what we can achieve. Machine learning/technology is not going to replace human but rather augment human intelligence with insights Similar to the role technology [e.g. Internet, Social Media] played/is-playing in 80,s, 90s and in the new Millennium, Machine Learning / AI is going to redefine what we do and how we do it Why should I care? 7
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  Why Now? WhatHappened? 8 Data Computing Power Statistical Methods Ability to capture and access to more and more Data!! Yes Data!! N fold increase in computing power to process data. Think about your smartphone and what all you can do with it. Yes the servers are more powerful as well.. Continuous research!! Algorithm implementations in PA software packages. Anybody heard about Data Scientists and ML courses? New Use Cases
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  9 MACHINE LEARNING
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  Let’s look underthe hood… 10 Data Featurize Normalize Filtering Imputation Featurize Data Fusion Training/ Testing Analysis/ Insights Use / Implement Input pre-processing Training / optimization Post Analysis Structured Un- structured Dimension Reduction
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   Supervised –Provide the algorithm with both the input and output and let it learn from it. Classification is a typical application of supervised algorithms. It can also be used to make numeric predictions (using Linear Regression Model) – Spam vs Ham  Un-Supervised – Only provide the input and let the algorithm learn from the data, discover patterns [Clustering/ Segmentation Analysis] – Market Basket Analysis for a grocery chain – Fraudulent Behavior  Meta Learners – is not tied to a specific learning but rather focus on learning how to learn more effectively (not covered in this presentation) Types of Machine Learning Algorithms 11
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  Learning Algorithms 12 Model LearningTask Supervised Nearest Neighbor Classification Naïve Bayes Classification Decision Trees Classification Classification Rule Learners Classification Linear Regression Numeric Predictions Regression Trees Numeric Predictions Model Trees Numeric Predictions Neural Networks Dual Use SVM (Support Vector Machines) Dual Use Un-Supervised Association Rules Pattern Detection K-Means Clustering Clustering
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   K NearestNeighbor  Decision Trees  Neural Network  Support Vector Machines  Regression Models Lets take a closer look at a few of them… 13
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   Classification Algorithm Lazy Learner, Non Parametric – No assumption about the underlying data set  No training based on data  K – Determine the K value ~ sqrt of the Data Set  Distance Function – Euclidian (Multi Dimensional)/ Cosine etc. K Nearest Neighbor 14 Sweetness Acidity W3 W2 W1 W4 W9 W5 W10 W7 W6 W8 W? Dist (W5, W?) = SQRT ((W5x – W?x)2 + (W5y – W?y)2) Or Dist (W2, W?) = SQRT ((W2x – W?x)2 + (W2y – W?y)2) Distance Function Classifying wines….
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   Decision treesutilize tree structure to model the relationships among the features and potential outcomes  Greedy Learners  Is easy to understand / explain how it works (except when it becomes really large)  Built using a heuristic called recursive partitioning / Divide and Conquer  Increase homogeneity in the data set – Decrease Entropy / Variablity  Very powerful and applicable to both small and large datasets  Easy to overfit / underfit (Tree decision points/nodes) Should not be used for data sets with large number of nominal / numeric features as it ends up creating extremely complex decision trees Decision Trees 15
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   The splitis done along the axis / feature set one at a time (e.g. do it by sweetness than by acidity)  Ensure that you are accounting for pruning the tree to control the size (pre / post pruning)  Optimize by cost Decision Tree (Example) 16 Root Decision Node Leaf Sweetness Acidity W3 W2 W1 W4 W9 W5 W10 W7 W6 W8 Sweetness Acidity
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  Neural Networks* –Black Box Methods 17 • Network Topology – Number of Layers, Nodes per layer and connection between them (Feedback/Feed-forward) • Activation Function – transforms a neurons combined input signals into single output signal and send it out • Training Algorithm – Determines how to set connection weights in order to inhibit or activate a node based on the input signal AF *The above is sample network that shows a one way flow, Deep NN flow both ways  Model built mimic the structure of brains. 3 pieces: Neural Networks are very powerful but prone to over-fitting!!
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   Models builttaking into consideration multidimensional surfaces to define the relationship between features and outcomes SVM – Support Vector Machines 18 Sweetness Acidity W3 W2 W1 W4 W9 W5 W10 W7 W6 W8 • Hyper-plane – Flat surface in a high dimensional space that separates out the different observations [Example – Wines shown in 2 dimensions but one can use multiple dimensions to separate the wines] • Maximum Margin Hyper-plane – A plane that creates the greatest separation between the two or more classes – Refer to the bars in the picture • SVM uses Vector Geometry to identify the planes that separate out the different classes of observations  SVMs are usually not prone to noise or over-fitting and fairly popular among Data Scientists due to the number of implementations available across different packages
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   Linear Regression– 1 numeric variable  Multi Linear Regression – 2 or more numeric variable  Logistic Regression – model binary categorical outcome  Poisson Regression – models integer count data  Multinomial Logistic Regression – models categorical outcome/ used for classification  Generalized Linear Models – Models that can be generalized to other patterns Key Difference between Machine Learning and Regression modeling is the feature selection and model specification is driven by the user Regression Models 19
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   Regression Treemodels do not use linear regression methods to make predictions. They may use the average value of the examples that reach a leaf  Model Trees are different from Regression trees as at each leaf, a multiple linear regression model is built from the examples reaching that node – Combines the strengths of decision trees with the ability to model numeric data – Uses automatic feature selection and can be used with a data set that has large number of features – Usually is more difficult to understand but does not need advanced statistical knowledge to interpret Regression Tree and Model Trees 20
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  DID I LOSEYOU IN THE SVM HYPER- PLANES?  BACK TO REALITY!! 21
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   Define Scopeand Objectives… – What are you trying to do and why?  Remember Data is key – Do not under-estimate the work that needs to go in to prep data for the models [historical and on-going]  Modeling is “Sexy” but not the be-all /end-all of this process  Yes you need to test the model results too - Does it make sense? – Does it really make sense? Try to dig into the why?  And do not forget on how will you operationalize the findings if any? – Using the insights is easier said that done!! Now the most difficult to part – Need a process to review the results of how you are using the insights and if it is delivering value!! OK…How do I get started? 22
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   Do notover fit the model or try to model the noise [Google to find out more]  Try to understand what the model is saying and why?  Never hesitate to question the findings?  If it is too good to be true, than it is not true!! Be skeptic, really skeptic of Model Results 23
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   Separate Trainingand Testing Data Sets – There are a number of ways to do this…  Regression and Classification model output – Compare predicted to actual results – Yes, you can slice/dice the data to get more insights – Use Confusion Matrix [Precision Rate, Recall Rate, Flag Rate] Model Testing 24
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   Residuals –summary statistics for the errors in our predictions [True Value – Predicted Value]  P-Value – Estimate of the probability that the true coefficient is zero given the value of the estimate  Multiple R Squared Value – Measures how well our model as a whole explains the values of the dependent variable (1.0 implies it is able to explain the result completely. Good luck with that…) Evaluating Regression Models 25
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  Model Testing –Confusion Matrix 26 TRUE FASLE TRUE TRUE POSITIVE FALSE NEGATIVE FALSE FALSE POSITIVE TRUE NEGATIVE MODEL Predictions ACTUAL RECAL TP / (TP+FN) PRECISION TP / (TP + FP) F L A G % P R E C I S I O N % RECALL % Flag Rate
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  Are we donehere? Need my Chai… 27 https://medium.com/swlh/the-7-best-data-science-and-machine-learning- podcasts-e8f0d5a4a419#.rl7nhqwje http://www.kdnuggets.com/2015/07/sameer-chopra-training-resources-predictive- analytics.html Feel free to share / use this presentation but not before you leave me a feedback!!