HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box 1: RegressionRegression is a supervised machine learning technique used to predict numeric values.Scenarios for regression machine learning modelsRegression machine learning models are used in many industries. A few scenarios are:Using characteristics of houses, such as square footage and number of rooms, to predict home prices. Using characteristics of farm conditions, such as weather and soil quality, to predict crop yield. Using characteristics of a past campaign, such as advertising logs, to predict future advertisement clicks.Note: Loan chargeoff prediction with SQL Server, example.Solution detailsA charged off loan is a loan that is declared by a creditor (usually a lending institution) that an amount of debt is unlikely to be collected, usually when the loan repayment is severely delinquent by the debtor. Given that high chargeoff has a negative impact on lending institutions' year-end financials, lending institutions often monitor loan chargeoff risk very closely to prevent loans from getting charged-off.There are multiple benefits for lending institutions to equip with loan chargeoff prediction data. Charging off a loan is the last resort that the bank will do on a severely delinquent loan, with the prediction data at hand, the loan officer could offer personalized incentives like lower interest rate or longer repayment period to help customers to keep making loan payments and thus prevent the loan of getting charged off. To get to this type of prediction data, often credit unions or banks manually handcraft the data based on customers' past payment history and performed simple statistical regression analysis.
https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine- learning-designer/2-regression-scenarios
Box: thresholdDecision Threshold In Machine LearningWhat is Decision Threshold?sklearn does not let us set the decision threshold directly, but it gives us the access to decision scores (Decision function o/p) that is used to make the prediction. We can select the best score from decision function output and set it as Decision Threshold value and consider all those Decision score values which are less than this Decision Threshold as a negative class ( 0 ) and all those decision score values that are greater than this Decision Threshold value as a positive class ( 1 ).Example output:Incorrect:* AUC metricAUC is the Area under the Receiver Operating Characteristic Curve.Objective: Closer to 1 the betterRange: [0, 1]* confusion matrixConfusion matrices provide a visual for how a machine learning model is making systematic errors in its predictions for classification models. The word "confusion" in the name comes from a model "confusing" or mislabeling samples. A cell at row i and column j in a confusion matrix contains the number of samples in the evaluation dataset that belong to class C_i and were classified by the model as class C_j.Confusion matrix for a bad model* ROC curveThe receiver operating characteristic (ROC) curve plots the relationship between true positive rate (TPR) and false positive rate (FPR) as the decision threshold changes. The ROC curve can be less informative when training models on datasets with high class imbalance, as the majority class can drown out contributions from minority classes.ROC curve for a bad model
https://www.geeksforgeeks.org/decision-threshold-in-machine-learning/ https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
Box: featuresML label vs featureAt the first glance labels and features in machine learning may seem like they describe very similar, if not identical, concepts, but this is far from the truth. As discussed above, a label represents an output value, while a feature is an input value that describes the characteristics of such labels in datasets.For example, you have a completed ML model, which has been pre-trained with datasets of dog breeds, in which their characteristics (features) and corresponding breeds (labels) have been specified. Now the algorithm built into this model should be able to determine the type of dog (label) by the traits (features) you provide to it.When ML models are used in real life, experts present input data with features to the computer. For example, in a factory, a conveyor belt moves a lot of tomatoes. The ML model captures the features of tomatoes by employing computer vision tools. If the model recognizes such tomato features as its color which is brown or black the label "rotten" is assigned to the fruit, if the color is red, yellow, or orange it means that the tomato is fresh. It is worth mentioning that this is a simplified approximated description of the process.The features have to be uploaded to the computer system as input so that the ML algorithms can generate a label as an output. This is the difference between ML labels and features. The more and better the labeled training dataset was, the better the model will predict labels by features.
https://toloka.ai/blog/machine-learning-labels-and-features/
DRAG DROP (Drag and Drop is not supported)Match the machine learning models to the appropriate descriptions.To answer, drag the appropriate model from the column on the left to its description on the right. Each model may be used once, more than once, or not at all.Note: Each correct match is worth one point.Select and Place:
Box 1: RegressionTypes of Machine LearningRegression: used to predict continuous value e.g., priceBox 2: ClassificationClassification: used to determine binary class label e.g., whether an animal is a cat or a dogBox 3: ClusteringClustering: determine labels by grouping similar information into label groups, for instance grouping music into genres based on its characteristics.
https://techcommunity.microsoft.com/t5/educator-developer-blog/getting-started-with-using-visual-machine- learning-tools-for/ba-p/3578397
You are building a tool that will process images from retail stores and identify the products of competitors.The solution must be trained on images provided by your company.Which Azure AI service should you use?
Answer(s): B
Azure AI Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models. An image identifier applies labels to images, according to their visual characteristics.Each label represents a classification or object. Unlike the Azure AI Vision service, Custom Vision allows you to specify your own labels and train custom models to detect them.
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview
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