DRAG DROP (Drag and Drop is not supported)Match the machine learning tasks to the appropriate scenarios.To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.Note: Each correct selection is worth one point.Select and Place:
Answer(s): A
Box 1: Model evaluationThe Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.Box 2: Feature engineeringFeature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.Box 3: Feature selectionIn machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
https://www.baeldung.com/cs/feature-vs-label https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get- good-at-it/
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
Answer(s): B
In the most basic sense, regression refers to prediction of a numeric target.Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
You have a dataset that contains information about taxi journeys that occurred during a given period.You need to train a model to predict the fare of a taxi journey.What should you use as a feature?
The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label.Example:The provided data set contains the following columns:vendor_id: The ID of the taxi vendor is a feature.rate_code: The rate type of the taxi trip is a feature.passenger_count: The number of passengers on the trip is a feature.trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.trip_distance: The distance of the trip is a feature.payment_type: The payment method (cash or credit card) is a feature.fare_amount: The total taxi fare paid is the label.
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices
You need to predict the sea level in meters for the next 10 years.Which type of machine learning should you use?
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