You must prioritize the columns of data for

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JavaScript: The Web Warrior Series
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Chapter 6 / Exercise 1
JavaScript: The Web Warrior Series
Vodnik
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You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships. You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns. Model training Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the
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JavaScript: The Web Warrior Series
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Chapter 6 / Exercise 1
JavaScript: The Web Warrior Series
Vodnik
Expert Verified
The safer , easier way to help you pass any IT exams.15/ 35correct metric to investigate the model’s accuracy and replicate the findings.You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful. You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs. Testing You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city’s main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process. When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent. Data visualization You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results. You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another. DRAG DROP You need to implement early stopping criteria as suited in the model training requirements.

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