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groupings within data sets.Some common examples of cluster analysis classifications would include the following:Socioeconomic tiers Income, education, profession, age, number of children, size of city orresidence, and so on.Psychographic data Personal interests, lifestyle, motivation, values, involvement.Social network graphs Groups of people related to you by family, friends, work, schools,professional associations, and so on.Purchasing patterns Price range, type of media used, intensity of use, choice of retail outlet,fidelity, buyer or nonbuyer, buying intensity.The other type of approach to unsupervised machine learning is to use a reward system, rather thanany kind of teaching aids, as are commonly used in supervised learning. Positive and negative rewardsare used to provide feedback to the predictive model when it has been successful.The key to success in implementing this model is to enable the new model to make its predictionsbased solely on previous rewards and punishments for similar predictions made on similar data sets.Unsupervised machine learning algorithms can be a powerful asset when there is an easy way toassign feedback values to actions. Clustering can be useful when there is enough data to form clustersto logically delineate the data. The delineated data then make inferences about the groups andindividuals in the cluster.Deploying a prediction modelIn the world of Azure Machine Learning, the deployment of a new prediction model takes the form ofexposing a web service on the public Internet via Microsoft Azure. The web service can then be invokedvia the Representational State Transfer (REST) protocol.Azure Machine Learning web services can be called via two different exposed interfaces:Single, request/response-style calls.“Batch” style calls, where multiple input records are passed into the web service in a single calland the corresponding response contains an output list of predictions for each input record.When a new machine learning prediction model is exposed on the Web, it performs the followingoperations:New input data is passed into the web service in the form of a JavaScript Object Notation (JSON)payload.34
The web service then passes the incoming data as inputs into the Azure Machine Learningprediction model engine.The Azure Machine Learning model then generates a new prediction based on the input dataand returns the new prediction results to the caller via a JSON payload.Show me the moneyIf we step back from the previous example of predicting a person’s income level, to get the forest viewinstead of the tree view, you can quickly see how this type of predictive knowledge about a person’sincome level would be hugely beneficial to the success of any marketing campaign.Take, for example, the most basic postal marketing campaign with the goal of targeting individualswith incomes greater than $50,000. The campaign might start by simply employing a “brute force”method of marketing, blindly blanketing a specific set of zip codes with marketing collateral in the dailymail and then hoping for the best. It is probable that the campaign will reach the desired targetaudience. But, the downside of this approach is that there is a real, financial cost for each item mailed,and that will erode any profit margins that might be achieved.Consider the huge advantage that a finely tuned Azure Machine Learning prediction model couldadd to this marketing campaign scenario. By targeting the same number of individuals and leveragingthe power of predictive analytics, the success rate of almost any marketing campaigns could easily beimproved. In today’s business world, using a prediction engine that can dramatically improve the oddsof success by filtering out high-probability candidates would pay for itself in a very short time!Figure 2-7 illustrates the effect that the use of predictive analytics can have on a simple postalmarketing campaign. By increasing the marketing campaign’s results from 5 percent to 20 percentthrough the use of predictive analytics, a significant impact can be achieved on the bottom line.FIGURE 2-7 A simple example postal marketing campaign improvement with the use of predictive analytics.The key to success in this example is to create a new model that can accurately make predictionsbased on a fresh new set of input data. The new inference model is then used to predict the likelihoodof the outcome, in the form of an accuracy level or confidence factor. In this case, accuracy is usuallyexpressed in terms of a percentage factor and is calculated to be between 0 and 1. The closer theaccuracy level approaches 1, the higher the chances of a successful prediction.35
Page 1 and 2: Azure MachineLearningMicrosoft Azur
Page 3 and 4: Hear aboutit first.Get the latest n
Page 5 and 6: PUBLISHED BYMicrosoft PressA divisi
Page 7 and 8: Supervised learning ...............
Page 9 and 10: KNN: K nearest neighbor algorithm .
Page 11 and 12: IntroductionMicrosoft Azure Machine
Page 13 and 14: Chapter 5, “Regression analytics,
Page 15 and 16: If you need additional support, ema
Page 17 and 18: Chapter 1Introduction to the scienc
Page 19 and 20: pack. By combining these deep psych
Page 21 and 22: Predictive analyticsPredictive anal
Page 23 and 24: Everyday examples of predictive ana
Page 25 and 26: 22 million: Average number of mail
Page 27 and 28: it began to learn at an exponential
Page 29 and 30: Chapter 2Getting started with Azure
Page 31 and 32: Data It’s all about the data. Her
Page 33 and 34: The concept of supervised learning
Page 35 and 36: to determine the model’s accuracy
Page 37: FIGURE 2-6 Testing the new predicti
Page 41 and 42: Creating new prediction models usin
Page 43 and 44: Data center location Defines the ph
Page 45 and 46: This is a free Azure offer that is
Page 47 and 48: In terms of where you can host an A
Page 49 and 50: An Azure Machine Learning workspace
Page 51 and 52: Machine Learning Workspace features
Page 53 and 54: Machine Learning Studio and then pu
Page 55 and 56: Number of instances in the dataset
Page 57 and 58: Create a new Azure Machine Learning
Page 59 and 60: experiment. Figure 3-13 shows the A
Page 61 and 62: FIGURE 3-16 Visualization of the Ad
Page 63 and 64: The workclass field is designated a
Page 65 and 66: Figure 3-20 displays a screenshot o
Page 67 and 68: FIGURE 3-22 Opening the column sele
Page 69 and 70: FIGURE 3-24 Training our new Azure
Page 71 and 72: FIGURE 3-26 Visualizing the results
Page 73 and 74: Evaluate the modelOne of the most c
Page 75 and 76: Receiver Operator Characteristic (R
Page 77 and 78: FIGURE 3-31 After the Prepare Web S
Page 79 and 80: If we click the toggle button again
Page 81 and 82: FIGURE 3-37 The Azure Machine Learn
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Page 85 and 86: provide access to data over the Web
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FIGURE 3-47 A sample Response Body
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Sample Code Here’s the best part:
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Testing the Azure Machine Learning
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FIGURE 3-53 Processing the Azure Ma
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ce-to-azure-marketplace/http://data
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The following is a listing of the a
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FIGURE 4-2 Select the specific impl
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FIGURE 4-5 Sample code for invoking
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FIGURE 4-7 Invoking the NuGet Packa
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FIGURE 4-9 Sample C# code to invoke
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Now watch what happens if we replac
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To do this, simply click the menu o
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FIGURE 4-16 Invoking the Azure Mach
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They fall short, however, in terms
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FIGURE 4-19 Selecting an Empty ASP.
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FIGURE 4-21 Selecting a Web Form it
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Validating the user inputTo further
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FIGURE 4-26 Invoking the Azure Mach
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Create a web service using ASP.NET
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FIGURE 4-29 Selecting the Visual St
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FIGURE 4-31 Adding a new item to th
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FIGURE 4-33 C# class data structure
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FIGURE 4-35 Adding the empty scaffo
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FIGURE 4-38 Installing the NuGet pa
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FIGURE 4-41 The IncomePredictionCon
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{Inputs = new Dictionary<string, St
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}incomePredResult.Sex = mlResultsAr
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FIGURE 4-44 A jQuery Mobile web app
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var iSex = document.getElementById(
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Chapter 5Regression analyticsIn the
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claims. It is easily seen how these
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Note that in this case, the Azure M
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FIGURE 5-4 The Upload A New Dataset
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FIGURE 5-7 Azure ML Studio: Selecti
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FIGURE 5-9 Selecting the option to
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FIGURE 5-12 The Clean Missing Data
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to .80. In this context, .80 means
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FIGURE 5-16 The automobile price pr
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FIGURE 5-18 The option to Visualize
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After clicking Publish Web Service
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FIGURE 5-23 The Azure ML Studio Reg
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SummaryIn this chapter, we took a d
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unsupervised machine learning, the
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Cluster analysis is not necessarily
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Milk Annual spending on milk produc
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Now that we have the Reader module
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After placing the Train Clustering
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he Metric parameter offers two opti
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the results of our K-means clusteri
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Train Clustering Model module remai
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Removes the Train Clustering Model,
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FIGURE 6-24 The wholesale customers
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FIGURE 6-26 The API help sample cod
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FIGURE 6-28 The CSV output file ope
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Chapter 7The Azure ML Matchboxrecom
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Back in the good old days of video
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webpages, recipes, or even other pe
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FIGURE 7-1 The restaurant ratings s
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FIGURE 7-2 Populating the three res
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Now it’s time to add the Train Ma
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Armed with this knowledge, let’s
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The last module to add to our Match
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prediction model will return a set
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FIGURE 7-17 The completed experimen
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FIGURE 7-19 The Azure Machine Learn
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Chapter 8Retraining Azure ML models
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Figure 8-2 illustrates the two appr
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Once you have re-created or located
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as a web service. After processing,
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After adding the three additional W
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FIGURE 8-9 Azure Machine Learning W
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FIGURE 8-11 A sample Azure Machine
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FIGURE 8-13 The two endpoints for t
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FIGURE 8-15 The Azure Machine Learn
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ResourcesFor more information about
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