You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.You must predict the fare of a taxi trip.You need to select performance metrics to correctly evaluate the regression model.Which two metrics can you use? Each correct answer presents a complete solution?NOTE: Each correct selection is worth one point.
Answer(s): A,D
RMSE and R2 are both metrics for regression models.A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.Incorrect Answers:C, E: F-score is used for classification models, not for regression models.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are using Azure Machine Learning to run an experiment that trains a classification model.You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.Solution: Run the following code:Does the solution meet the goal?
Answer(s): B
Python printing/logging example:logging.info(message)Destination: Driver logs, Azure Machine Learning designer
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines
Explanation Use a solution with logging.info(message) instead.Note: Python printing/logging example:logging.info(message)Destination: Driver logs, Azure Machine Learning designer
You use the following code to run a script as an experiment in Azure Machine Learning:You must identify the output files that are generated by the experiment run.You need to add code to retrieve the output file names.Which code segment should you add to the script?
You can list all of the files that are associated with this run record by called run.get_file_names()
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments
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