HOTSPOT (Drag and Drop is not supported)You create a binary classification model to predict whether a person has a disease.You need to detect possible classification errors.Which error type should you choose for each description? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: True Positive A true positive is an outcome where the model correctly predicts the positive classBox 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.Box 3: False Positive A false positive is an outcome where the model incorrectly predicts the positive class.Box 4: False Negative A false negative is an outcome where the model incorrectly predicts the negative class.Note: Let's make the following definitions:"Wolf" is a positive class."No wolf" is a negative class.We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative
HOTSPOT (Drag and Drop is not supported)You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:-The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.-Batch size must be 16, 32 and 64.-Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.How should you complete the code segment? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: normal(10,3)Box 2: choice(16, 32, 64)Box 3: uniform(0.05, 0.1)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.Example:from azureml.train.hyperdrive import RandomParameterSamplingparam_sampling = RandomParameterSampling( {"learning_rate": normal(10, 3),"keep_probability": uniform(0.05, 0.1),"batch_size": choice(16, 32, 64)}
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.Which parameter and value pair should you use in the AutoMLConfig class?
Featurization str or FeaturizationConfigValues: 'auto' / 'off' / FeaturizationConfigIndicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.Numeric: Impute missing values, cluster distance, weight of evidence.DateTime: Several features such as day, seconds, minutes, hours etc.Text: Bag of words, pre-trained Word embedding, text target encoding.
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig
DRAG DROP (Drag and Drop is not supported)You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.You need to use the designer to create a pipeline that includes steps to perform the following tasks:-Select the training features using the pandas filter method.-Train a model based on the naive_bayes.GaussianNB algorithm.-Return only the Scored Labels column by using the query-SELECT [Scored Labels] FROM t1;Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
Box 1: Two-Class Neural Network The Two-Class Neural Network creates a binary classifier using a neural network algorithm.Train a model based on the naive_bayes.GaussianNB algorithm.Box 2: Execute python script Select the training features using the pandas filter methodBox 3: Select Columns in DataSetReturn only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1;
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network
You are building a regression model for estimating the number of calls during an event.You need to determine whether the feature values achieve the conditions to build a Poisson regression model.Which two conditions must the feature set contain? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): B,D
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:- The response variable has a Poisson distribution.- Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.- A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression
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