You need to evaluate a classification model. Which metric can you use? ​​ Mean squared error (MSE)PrecisionSilhouette3. In deep learning, what is the purpose of a loss function? To remove data for which no known label values are providedTo evaluate the aggregate difference between predicted and actual label valuesTo calculate the cost of training a neural network rather than a statistical model4. What does automated machine learning in Azure Machine Learning enable you to do? Automatically deploy new versions of a model as they're trainedAutomatically provision Azure Machine Learning workspaces for new data scientists in an organizationAutomatically run multiple training jobs using different algorithms and parameters to find the best model

. What does automated machine learning in Azure Machine Learning enable you to do? Automatically deploy new versions of a model as they're trainedAutomatically provision Azure Machine Learning workspaces for new data scientists in an organizationAutomatically run multiple training jobs using different algorithms and parameters to find the best model

. You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service? Create an inference pipeline from the training pipelineAdd an Evaluate Model module to the training pipelineClone the training pipeline with a different name

You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model's performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.3. What can you conclude about the model? The model can explain 30% of the variance between true and predicted labels.The model predicts accurately for 70% of test cases.The model performs worse than random guessing.

In deep learning, what is the purpose of a loss function? To remove data for which no known label values are providedTo evaluate the aggregate difference between predicted and actual label valuesTo calculate the cost of training a neural network rather than a statistical model

You're using Azure Machine Learning designer to create a training pipeline for a binary classification model. You've added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that wasn't used for training. What's another module should you add? Join DataSplit DataSelect Columns in Dataset