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 creating a new experiment in Azure Machine Learning Studio.One class has a much smaller number of observations than the other classes in the training set.You need to select an appropriate data sampling strategy to compensate for the class imbalance.Solution: You use the Principal Components Analysis (PCA) sampling mode.Does the solution meet the goal?
Answer(s): B
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.Incorrect Answers:The Principal Component Analysis module in Azure Machine Learning Studio (classic) is used to reduce the dimensionality of your training data. The module analyzes your data and creates a reduced feature set that captures all the information contained in the dataset, but in a smaller number of features.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/principal-component-analysis
You are performing feature engineering on a dataset.You must add a feature named CityName and populate the column value with the text London.You need to add the new feature to the dataset.Which Azure Machine Learning Studio module should you use?
Answer(s): C
Typical metadata changes might include marking columns as features.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata
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
Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model.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 scatter plot graphs the actual values in your data against the values predicted by the model. The scatter plot displays the actual values along the X-axis, and displays the predicted values along the Y-axis. It also displays a line that illustrates the perfect prediction, where the predicted value exactly matches the actual value.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion-matrix
You are solving a classification task.You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.You need to configure the k parameter for the cross-validation.Which value should you use?
Leave One Out (LOO) cross-validationSetting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.
HOTSPOT (Drag and Drop is not supported)You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.You create the following Python data frames:You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.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:
Answer(s): A
Box 1: PCA(n_components = 10)Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.Example:from sklearn.decomposition import PCApca = PCA(n_components=2) ;2 dimensionsprincipalComponents = pca.fit_transform(x)Box 2: pca fit_transform(X[, y]) fits the model with X and apply the dimensionality reduction on X.Box 3: transform(x_test)transform(X) applies dimensionality reduction to X.
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
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