You are evaluating a completed binary classification machine learning model.You need to use the precision as the evaluation metric.Which visualization should you use?
Answer(s): D
Incorrect Answers:A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.C: A box plot lets you see basic distribution information about your data, such as median, mean, range and quartiles but doesn't show you how your data looks throughout its range.
https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/
You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.Which three actions must you perform? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): A,D,F
AD:primary_metric_name="accuracy",primary_metric_goal=PrimaryMetricGoal.MAXIMIZEOptimize the runs to maximize "accuracy". Make sure to log this value in your training script.Note:primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script. primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.
DRAG DROP (Drag and Drop is not supported)You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
Answer(s): A
Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.Step 2: Add a dataset to the experimentStep 3: Add a Split Data module to create training and test dataset.To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance
HOTSPOT (Drag and Drop is not supported)You are using the Hyperdrive feature in Azure Machine Learning to train a model.You configure the Hyperdrive experiment by running the following code:For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Yes In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.Box 2: Yes learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.Box 3: No keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.Box 4: No number_of_hidden_layers takes on one of the values [3, 4, 5].
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
You are performing a filter-based feature selection for a dataset to build a multi-class classifier by using Azure Machine Learning Studio.The dataset contains categorical features that are highly correlated to the output label column.You need to select the appropriate feature scoring statistical method to identify the key predictors.Which method should you use?
Answer(s): C
Pearson's correlation statistic, or Pearson's correlation coefficient, is also known in statistical models as the r value. For any two variables, it returns a value that indicates the strength of the correlationPearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.Incorrect Answers:C: The two-way chi-squared test is a statistical method that measures how close expected values are to actual results.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection https://www.statisticssolutions.com/pearsons-correlation-coefficient/
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